Paper Digest: IJCAI 2016 Highlights

July 10, 2016May 9, 2020 admin

International Joint Conference on Artificial Intelligence (IJCAI) is one of the top artificial intelligence conferences in the world. In 2016, it is to be held in New York, USA.

To help the community quickly catch up on the work presented in this conference, Paper Digest Team processed all accepted papers, and generated one highlight sentence (typically the main topic) for each paper. Readers are encouraged to read these machine generated highlights / summaries to quickly get the main idea of each paper.

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TABLE 1: IJCAI 2016 Papers

	Title	Authors	Highlight
1	Interdependent Scheduling Games	Andres Abeliuk, Haris Aziz, Gerardo Berbeglia, Serge Gaspers, Petr Kalina, Nicholas Mattei, Dominik Peters, Paul Stursberg, Pascal Van Hentenryck, Toby Walsh	We propose a model of interdependent scheduling games in which each player controls a set of services that they schedule independently.
2	Scalable Greedy Algorithms for Task/Resource Constrained Multi-Agent Stochastic Planning	Pritee Agrawal, Pradeep Varakantham, William Yeoh	In this paper, we address these two limitations by introducing a generic model for task/resource constrained multi-agent stochastic planning, referred to as TasC-MDPs.
3	Coco: Runtime Reasoning about Conflicting Commitments	Nirav Ajmeri, Jiaming Jiang, Rada Chirkova, Jon Doyle, Munindar P. Singh	We describe Coco, an approach for reasoning about which specific commitments apply to specific parties in light of general types of commitments, specific circumstances, and dominance relations among specific commitments.
4	Verifying Existence of Resource-Bounded Coalition Uniform Strategies	Natasha Alechina, Mehdi Dastani, Brian Logan	We consider the problem of whether a coalition of agents has a knowledge-based strategy to ensure some outcome under a resource bound.
5	On Truthful Mechanisms for Maximin Share Allocations	Georgios Amanatidis, Georgios Birmpas, Evangelos Markakis	In this work, we embark on a mechanism design approach and investigate the existence of truthful mechanisms.
6	Modeling and Reasoning about NTU Games via Answer Set Programming	Giovanni Amendola, Gianluigi Greco, Nicola Leone, Pierfrancesco Veltri	Modeling and Reasoning about NTU Games via Answer Set Programming
7	Randomized Social Choice Functions under Metric Preferences	Elliot Anshelevich, John Postl	We determine the quality of randomized social choice mechanisms in a setting in which the agents have metric preferences: every agent has a cost for each alternative, and these costs form a metric.
8	Generalized Discrete Preference Games	Vincenzo Auletta, Ioannis Caragiannis, Diodato Ferraioli, Clemente Galdi, Giuseppe Persiano	In this paper, we define and study the novel class of generalized discrete preference games.
9	Computing Pareto Optimal Committees	Haris Aziz, Jérôme Lang, Jérôme Monnot	Among the various criteria put forth for desirability of a committee, Pareto optimality is a minimal and important requirement.As asking agents to specify their preferences over exponentially many subsets of alternatives is practically infeasible,we assume that each agent specifies a weak order on single alternatives, from which a preference relation over subsets is derived using some preference extension.We consider four prominent extensions (responsive, leximax, best, and worst).
10	Control of Fair Division	Haris Aziz, Ildikó Schlotter, Toby Walsh	For two agents, we present polynomial-time algorithms for adding or deleting the minimum number of items to achieve ordinal envy-freeness.
11	A Characterization of Voting Power for Discrete Weight Distributions	Yoram Bachrach, Yuval Filmus, Joel Oren, Yair Zick	We focus on a model where the agent weights originate from a stochastic process, resulting in weight uncertainty.
12	Misrepresentation in District Voting	Yoram Bachrach, Omer Lev, Yoad Lewenberg, Yair Zick	We define the Misrepresentation Ratio which quantifies the deviation from proportional representation in a district-based election, and provide bounds for this ratio under various voting rules.
13	Conditional and Sequential Approval Voting on Combinatorial Domains	Nathanaël Barrot, Jérôme Lang	We define a family of rules for approval-based voting on combinatorial domains, where voters cast conditional approval ballots, allowing them to approve values of a variable conditionally on the values of other variables.
14	On Logics of Strategic Ability Based on Propositional Control	Francesco Belardinelli, Andreas Herzig	In this paper we provide a contribution to the comparison of two popular frameworks: Concurrent Game Structures (CGS) and Coalition Logic of Propositional Control (CLPC).
15	Complexity of Efficient and Envy-Free Resource Allocation: Few Agents, Resources, or Utility Levels	Bernhard Bliem, Robert Bredereck, Rolf Niedermeier	We study the problem of finding a Pareto-efficient and envy-free allocation of a set of indivisible resources to a set of agents with monotonic preferences, either dichotomous or additive.
16	The Complexity of Playing Durak	édouard Bonnet	We show that, even restricted to the perfect information two-player game, finding optimal moves is a hard problem.
17	Proving the Incompatibility of Efficiency and Strategyproofness via SMT Solving	Florian Brandl, Felix Brandt, Christian Geist	In this paper, we provide a computer-aided proof of a sweeping impossibility using these two conditions for randomized aggregation mechanisms.
18	To Give or Not to Give: Fair Division for Single Minded Valuations	Simina Brânzei, Yuezhou Lv, Ruta Mehta	In this paper we study the fair division problem for such agents, which is complex due to discontinuity and complementarities of preferences.
19	Pairwise Diffusion of Preference Rankings in Social Networks	Markus Brill, Edith Elkind, Ulle Endriss, Umberto Grandi	We introduce a model of preference diffusion in which agents in a social network update their preferences based on those of their influencers in the network, and we study the dynamics of this model.
20	Facility Location with Minimax Envy	Qingpeng Cai, Aris Filos-Ratsikas, Pingzhong Tang	Our goal is to minimize the maximum envy over all agents, which we will refer to as the minimax envy objective, while at the same time ensuring that agents will report their most preferred locations truthfully.
21	Achieving Proportional Representation in Conference Programs	Ioannis Caragiannis, Laurent Gourvès, Jérôme Monnot	On the positive side, we present polynomial-time algorithms that compute conference programs that have a social utility that is provably close to the optimal one (within constant factors).
22	Subset Selection via Implicit Utilitarian Voting	Ioannis Caragiannis, Swaprava Nath, Ariel D. Procaccia, Nisarg Shah	We extend this approach to the design of rules that select a subset of alternatives.
23	Trading on a Rigged Game: Outcome Manipulation in Prediction Markets	Mithun Chakraborty, Sanmay Das	We propose a new game-theoretic model that captures two aspects of real-world prediction markets: (1) agents directly affect the outcome the market is predicting, (2) some outcome-deciders may not participate in the market.
24	Congestion Games with Polytopal Strategy Spaces	Hau Chan, Albert Xin Jiang	In this work, we study congestion games in which each player’s strategy space can be described compactly using a set of linear constraints.
25	Robust Draws in Balanced Knockout Tournaments	Krishnendu Chatterjee, Rasmus Ibsen-Jensen, Josef Tkadlec	The above examples motivate the study of the computational problem of robust draws that guarantee a specified winning probability.
26	Verifying Pushdown Multi-Agent Systems against Strategy Logics	Taolue Chen, Fu Song, Zhilin Wu	In this paper, we investigate model checking algorithms for variants of strategy logic over pushdown multi-agent systems, modeled by pushdown game structures (PGSs).
27	Truthfulness of a Proportional Sharing Mechanism in Resource Exchange	Yukun Cheng, Xiaotie Deng, Qi Qi, Xiang Yan	In this paper, we consider the popular proportional sharing mechanism and discuss the incentives and opportunities of an agent to lie for personal gains in resource exchange game.
28	Better Strategyproof Mechanisms without Payments or Prior — An Analytic Approach	Yun Kuen Cheung	We revisit the problem of designing strategyproof mechanisms for allocating divisible items among two agents who have linear utilities, where payments are disallowed and there is no prior information on the agents’ preferences. For the case with two items: We provide a set of sufficient conditions for strategyproofness.
29	Approximating Value Equivalence in Interactive Dynamic Influence Diagrams Using Behavioral Coverage	Ross Conroy, Yifeng Zeng, Jing Tang	In this paper, we establish a principled framework to implement the VE techniques and propose an approximate method to compute VE of candidate models.
30	Selective Norm Monitoring	Natalia Criado, Jose M. Such	This paper proposes a novel mechanism to take full advantage of limited observation capabilities by selecting the agents to be monitored.
31	Elicitation for Preferences Single Peaked on Trees	Palash Dey, Neeldhara Misra	We extend this line of research and study preference elicitation for single peaked preferences on trees which is a strict superset of the domain of single peaked preferences.
32	Preference Elicitation for Single Crossing Domain	Palash Dey, Neeldhara Misra	In this paper, we consider the domain of single crossing preference profiles and study the query complexity of preference elicitation under various settings.
33	Complexity of Manipulation with Partial Information in Voting	Palash Dey, Neeldhara Misra, Y. Narahari	In this paper, we investigate manipulation with incomplete information.
34	Strategic Voting with Incomplete Information	Ulle Endriss, Svetlana Obraztsova, Maria Polukarov, Jeffrey S. Rosenschein	We explore how these limitations affect both the manipulability of voting rules and the dynamics of systems in which voters may repeatedly update their own vote in reaction to the moves made by others.
35	Voting-Based Group Formation	Piotr Faliszewski, Arkadii Slinko, Nimrod Talmon	We study a combinatorial problem formulated in terms of the following group-formation scenario.
36	Committee Scoring Rules: Axiomatic Classification and Hierarchy	Piotr Faliszewski, Piotr Skowron, Arkadii Slinko, Nimrod Talmon	We introduce decomposable rules, describe some of their applications, and show that the class of decomposable rules strictly contains the class of OWA-based rules.
37	How Hard Is It for a Party to Nominate an Election Winner?	Piotr Faliszewski, Laurent Gourvès, Jérôme Lang, Julien Lesca, Jérôme Monnot	We consider a Plurality-voting scenario, where the candidates are split between parties, and each party nominates exactly one candidate for the final election.
38	Digital Good Exchange	Wenyi Fang, Pingzhong Tang, Song Zuo	We prove that it is in general NP-complete to determine whether there exists a non-trivial pure Nash equilibrium where at least some agent chooses a nonempty subset of items.
39	Parallel Behavior Composition for Manufacturing	Paolo Felli, Brian Logan, Sebastian Sardina	In this paper, we extend classical AI behavior composition to manufacturing settings.
40	Opinion Dynamics with Local Interactions	Dimitris Fotakis, Dimitris Palyvos-Giannas, Stratis Skoulakis	For fixed neighborhoods, we present a simple randomized protocol that converges in expectation to the stable state of the Friedkin-Johnsen model.
41	Online Mechanism Design for Vehicle-to-Grid Car Parks	Enrico H. Gerding, Sebastian Stein, Sofia Ceppi, Valentin Robu	In this paper we consider the setting of a smart car park, where EVs come and go, and can be used for V2G while parked.
42	Moving in a Crowd: Safe and Efficient Navigation among Heterogeneous Agents	Julio Godoy, Ioannis Karamouzas, Stephen J. Guy, Maria Gini	Multi-agent navigation methods typically assume that all agents use the same underlying framework to navigate to their goal while avoiding colliding with each other.
43	A Network-Based Rating System and Its Resistance to Bribery	Umberto Grandi, Paolo Turrini	We study a rating system in which a set of individuals (e.g., the customers of a restaurant) evaluate a given service (e.g, the restaurant), with their aggregated opinion determining the probability of all individuals to use the service and thus its generated revenue.
44	Three Strategies to Success: Learning Adversary Models in Security Games	Nika Haghtalab, Fei Fang, Thanh H. Nguyen, Arunesh Sinha, Ariel D. Procaccia, Milind Tambe	We develop a new approach to learning the parameters of the behavioral model of a bounded rational attacker (thereby pinpointing a near optimal strategy), by observing how the attacker responds to only three defender strategies.
45	Reconfigurability in Reactive Multiagent Systems	Xiaowei Huang, Qingliang Chen, Jie Meng, Kaile Su	This paper proposes a logic-based approach, by generalising that of model checking multiagent systems, for the reconfigurability of reactive multiagent systems.
46	Probabilistic Matrix Inspection and Group Scheduling	Hooyeon Lee, Ashish Goel	Motivated by this example, we study the Probabilistic Matrix Inspection problem in which we are given a matrix of Bernoulli random variables that are mutually independent, and the objective is to determine whether the matrix contains a column consisting only of 1’s.
47	Catcher-Evader Games	Yuqian Li, Vincent Conitzer, Dmytro Korzhyk	In this paper, we introduce a general framework of catcher-evader games that can capture Bayesian security games as well as other game families of interest.
48	Asymptotically Tight Bounds for Inefficiency in Risk-Averse Selfish Routing	Thanasis Lianeas, Evdokia Nikolova, Nicolas E. Stier-Moses	In this paper, we provide the first lower bounds on the PRA.
49	Social Choice for Agents with General Utilities	Hongyao Ma, Reshef Meir, David C. Parkes	In this paper we study mechanisms in which we can use payments but where agents have non quasi-linear utility functions.
50	Incentivizing Reliability in Demand-Side Response	Hongyao Ma, Valentin Robu, Na Li, David C. Parkes	We study the problem of incentivizing reliable demand-response in modern electricity grids.
51	Allocating Indivisible Items in Categorized Domains	Erika Mackin, Lirong Xia	We initiate a research agenda of mechanism design for categorized domain allocation problems (CDAPs), where indivisible items from multiple categories are allocated to agents without monetary transfer and each agent gets at least one item per category.
52	Correlated Voting	Debmalya Mandal, David C. Parkes	Motivated by rank-order models from machine learning, we introduce two examples of positively-correlated models, namely Conditional Mallows and Conditional Plackett-Luce.
53	Silk: A Simulation Study of Regulating Open Normative Multiagent Systems	Mehdi Mashayekhi, Hongying Du, George F. List, Munindar P. Singh	We propose Silk, a mechanism wherein a generator monitors interactions among member agents and recommends norms to help resolve conflicts.
54	Incentivizing Intelligent Customer Behavior in Smart-Grids: A Risk-Sharing Tariff & Optimal Strategies	Georgios Methenitis, Michael Kaisers, Han La Poutré	This paper proposes an innovative risk-sharing tariff to incentivize intelligent customer behavior.
55	Dynamic Auctions with Bank Accounts	Vahab Mirrokni, Renato Paes Leme, Pingzhong Tang, Song Zuo	In particular, we study a set of auction in which the space of single-shot auctions is augmented with a structure that we call bank account, a real number for each node that summarizes the history so far.
56	SLIM: Semi-Lazy Inference Mechanism for Plan Recognition	Reuth Mirsky, Ya’akov (Kobi) Gal	This paper presents a new and efficient algorithm for online plan recognition called SLIM (Semi-Lazy Inference Mechanism).
57	Sequential Plan Recognition	Reuth Mirsky, Roni Stern, Ya’akov (Kobi) Gal, Meir Kalech	We propose a number of policies for the SPRP which use maximum likelihood and information gain to choose which plan to query.
58	Distributed Decoupling of Multiagent Simple Temporal Problems	Jayanth Krishna Mogali, Stephen F. Smith, Zachary B. Rubinstein	We propose a new distributed algorithm for decoupling the Multiagent Simple Temporal Network (MaSTN) problem. We pose the MaSTN decoupling problem as a distributed convex optimization problem subject to constraints having a block angular structure; we adapt existing variants of Alternating Direction Method of Multiplier (ADMM) type methods to perform decoupling optimally.
59	Role Assignment for Game-Theoretic Cooperation	Catherine Moon, Vincent Conitzer	In this paper, we focus on another aspect: when the agents are self-interested, careful role assignment is necessary to make cooperative behavior an equilibrium of the repeated game.
60	Core-Selecting Payment Rules for Combinatorial Auctions with Uncertain Availability of Goods	Dmitry Moor, Sven Seuken, Tobias Grubenmann, Abraham Bernstein	In this paper, we study the design of core-selecting payment rules for such domains.
61	Automated Mechanism Design without Money via Machine Learning	Harikrishna Narasimhan, Shivani Agarwal, David C. Parkes	Our goal is to find a mechanism that best approximates a given target function subject to a design constraint such as strategy-proofness or stability.
62	Trembling Hand Equilibria of Plurality Voting	Svetlana Obraztsova, Zinovi Rabinovich, Edith Elkind, Maria Polukarov, Nicholas R. Jennings	In this paper, we analyze TH equilibria of Plurality voting.
63	Distributed Breakout: Beyond Satisfaction	Steven Okamoto, Roie Zivan, Aviv Nahon	We propose Generalized DBA (GDBA) to span the 24 combinations in the three dimensions.
64	Mission Oriented Robust Multi-Team Formation and Its Application to Robot Rescue Simulation	Tenda Okimoto, Tony Ribeiro, Damien Bouchabou, Katsumi Inoue	In this paper, the focus is laid on the mission oriented robust multi-team formation problem.
65	Controlling Growing Tasks with Heterogeneous Agents	James Parker, Maria Gini	We propose solutions for assignment of physical tasks to heterogeneous agents when the costs of the tasks change over time.
66	Using Message-Passing DCOP Algorithms to Solve Energy-Efficient Smart Environment Configuration Problems	Pierre Rust, Gauthier Picard, Fano Ramparany	We consider environments in which smart devices equipped with limited communication and computation capabilities have to cooperate to self-configure their state in an energy-efficient manner, as to meet user-defined requirements.
67	An Online Mechanism for Ridesharing in Autonomous Mobility-on-Demand Systems	Wen Shen, Cristina V. Lopes, Jacob W. Crandall	For the purpose of promoting ridesharing, we hereby introduce a posted-price, integrated online ridesharing mechanism (IORS) that satisfies desirable properties such as ex-post incentive compatibility, individual rationality, and budget-balance.
68	Efficient Local Search in Coordination Games on Graphs	Sunil Simon, Dominik Wojtczak	In this paper we identify several natural classes of graphs for which a finite improvement or coalition-improvement path of polynomial length always exists, and, as a consequence, a Nash equilibrium or strong equilibrium in them can be found in polynomial time.
69	Assigning a Small Agreeable Set of Indivisible Items to Multiple Players	Warut Suksompong	We consider an assignment problem that has aspects of fair division as well as social choice.
70	Preserving Privacy in Region Optimal DCOP Algorithms	Tamir Tassa, Roie Zivan, Tal Grinshpoun	In this work we present a framework called RODA (Region-Optimal DCOP Algorithm) that encompasses the algorithms in the region-optimality family, and in particular any method for selecting groups.
71	Nash Equilibria and Their Elimination in Resource Games	Nicolas Troquard	We introduce a class of resource games where resources and preferences are described with the language of a resource-sensitive logic. We present two decision problems, the first of which is deciding whether an action profile is a Nash equilibrium.
72	An Empirical Game-Theoretic Analysis of Price Discovery in Prediction Markets	Elaine Wah, Sébastien Lahaie, David M. Pennock	In this paper, we employ simulation-based methods to study the role of a market maker in improving price discovery in a prediction market.
73	Hierarchical Approach to Transfer of Control in Semi-Autonomous Systems	Kyle Hollins Wray, Luis Pineda, Shlomo Zilberstein	We analyze the integrated model and show that it provides the required guarantees.
74	Coordinating Human-UAV Teams in Disaster Response	Feng Wu, Sarvapali D. Ramchurn, Xiaoping Chen	By exploiting the problem structure we propose a novel online planning algorithm to solve this model.
75	Efficient Resource Allocation for Protecting Coral Reef Ecosystems	Yue Yin, Bo An	In this paper, we view the problem of efficiently patrolling for protecting coral reef ecosystems from a game-theoretic perspective and propose 1) a new Stackelberg game model to formulate the problem of protecting MPAs, 2) two algorithms to compute the efficient protection agency’s strategies: CLP in which the protection agency’s strategies are compactly represented as fractional flows in a network, and CDOG which combines the techniques of compactly representing defender strategies and incrementally generating strategies.
76	Optimally Protecting Elections	Yue Yin, Yevgeniy Vorobeychik, Bo An, Noam Hazon	We introduce the problem of optimal protection against election control, where manipulation is allowed at the granularity of groups of voters (e.g., voting locations), through a denial-of-service attack, and the defender allocates limited protection resources to prevent control.
77	Decision-Making Policies for Heterogeneous Autonomous Multi-Agent Systems with Safety Constraints	Ruohan Zhang, Yue Yu, Mahmoud El Chamie, Behçet Açıkmese, Dana H. Ballard	In this paper, we devise algorithms that provide safe decision-making policies.
78	Towards a White Box Approach to Automated Algorithm Design	Steven Adriaensen, Ann Nowé	In this work, we propose an alternative white box approach, reformulating the algorithm design problem as a Markov Decision Process, capturing the intrinsic relationships between design decisions and their respective contribution to overall algorithm performance.
79	Action Selection for Hammer Shots in Curling	Zaheen Farraz Ahmad, Robert C. Holte, Michael Bowling	We survey existing methods for finding an optimal action in a continuous, low-dimensional space with stochastic outcomes, and adapt a method based on Delaunay Triangulation to our application.
80	Fast Solving Maximum Weight Clique Problem in Massive Graphs	Shaowei Cai, Jinkun Lin	In this work, we propose a new method for MWCP which interleaves between clique construction and graph reduction.
81	Packing Graphs with ASP for Landscape Simulation	Thomas Guyet, Yves Moinard, Jacques Nicolas, René Quiniou	This paper describes an application of Answer Set Programming (ASP) to crop allocation for generating realistic landscapes.
82	On the Topology of Genetic Algorithms	David Hofmeyr	This paper introduces a topological structure for the search space which is consistent with existing theory and practice for genetic algorithms, namely forma analysis.
83	Truncating Shortest Path Search for Efficient Map-Matching	Takashi Imamichi, Takayuki Osogami, Rudy Raymond	We propose a technique to truncate the shortest path search before finding all the shortest paths in the HMM-based map-matching without losing accuracy.
84	Relevance for SAT(ID)	Joachim Jansen, Bart Bogaerts, Jo Devriendt, Gerda Janssens, Marc Denecker	In this paper, we present a new notion called relevance.
85	Counting Linear Extensions of Sparse Posets	Kustaa Kangas, Teemu Hankala, Teppo Niinimäki, Mikko Koivisto	We present two algorithms for computing the number of linear extensions of a given n-element poset.
86	Comparing Search Algorithms Using Sorting and Hashing on Disk and in Memory	Richard E. Korf	We present experimental results on the sliding-tile puzzles, Rubik’s Cube, and the 4-peg Towers of Hanoi.
87	Limited Discrepancy AND/OR Search and Its Application to Optimization Tasks in Graphical Models	Javier Larrosa, Emma Rollon, Rina Dechter	In this paper we investigate the generalization of discrepancy-based search to AND/OR search trees and propose an extension of the Limited Discrepancy Search (LDS) algorithm.
88	FastLCD: Fast Label Coordinate Descent for the Efficient Optimization of 2D Label MRFs	Kangwei Liu, Junge Zhang, Peipei Yang, Kaiqi Huang	To solve the problem, this paper presents an efficient algorithm, named FastLCD.
89	Heuristics and Really Hard Instances for Subgraph Isomorphism Problems	Ciaran McCreesh, Patrick Prosser, James Trimble	We show how to generate "really hard’" random instances for subgraph isomorphism problems.
90	Markov Chain Analysis of Noise and Restart in Stochastic Local Search	Ole J. Mengshoel, Youssef Ahres, Tong Yu	We develop a simple SLS algorithm, MarkovSLS, with three search operators: greedy, noise, and restart.
91	Efficiently Finding Conceptual Clustering Models with Integer Linear Programming	Abdelkader Ouali, Samir Loudni, Yahia Lebbah, Patrice Boizumault, Albrecht Zimmermann, Lakhdar Loukil	In this paper, we decouple the problems of finding descriptions and forming clusters by first mining formal concepts (i.e. closed itemsets), and searching for the best k clusters that can be described with those itemsets.
92	Scalable Segment Abstraction Method for Advertising Campaign Admission and Inventory Allocation Optimization	Fei Peng, Tuomas Sandholm	Scalable Segment Abstraction Method for Advertising Campaign Admission and Inventory Allocation Optimization
93	Improved Heuristic and Tie-Breaking for Optimally Solving Sokoban	André G. Pereira, Robert Holte, Jonathan Schaeffer, Luciana S. Buriol, Marcus Ritt	We present a novel admissible pattern database heuristic (D) and tie-breaking rule (L) for Sokoban, allowing us to increase the number of optimally solved standard Sokoban instances from 20 to 28 and the number of proved optimal solutions from 25 to 32 compared to previous methods.
94	An Approximation Algorithm for the Subpath Planning Problem	Masoud Safilian, S. Mehdi Hashemi, Sepehr Eghbali, Aliakbar Safilian	By casting SPP to a graph routing problem, we propose a deterministic 2-approximation algorithm finding near optimal solutions, which runs in O(n3) time.
95	External Memory Bidirectional Search	Nathan R. Sturtevant, Jingwei Chen	We propose a method of delayed solution detection that makes external bidirectional search more efficient.
96	Canonical Orderings on Grids	Nathan R. Sturtevant, Steve Rabin	But, the approach itself is actually a set of diverse ideas applied together.
97	Monte Carlo Tree Search in Continuous Action Spaces with Execution Uncertainty	Timothy Yee, Viliam Lisy, Michael Bowling	We propose a new Monte Carlo tree search (MCTS) algorithm specifically designed for exploiting an execution model in this setting.
98	Multiple Constraint Acquisition	Robin Arcangioli, Christian Bessiere, Nadjib Lazaar	In this paper, we provide a new approach that is able to learn a maximum number of constraints violated by a given negative example.
99	Ranking Constraints	Christian Bessiere, Emmanuel Hebrard, George Katsirelos, Zeynep Kiziltan, Toby Walsh	For both ranking and correlation constraints, we propose efficient filtering algorithms and decompositions, and report experimental results demonstrating the promise of our proposed approach.
100	Bias in Algorithm Portfolio Performance Evaluation	Chris Cameron, Holger H. Hoos, Kevin Leyton-Brown	We propose an alternative VBS performance measure by (1) empirically obtaining the solver with best expected performance for each instance and (2) taking bootstrap samples for this solver on every instance, to obtain a confidence interval on VBS performance.
101	Constraint Acquisition with Recommendation Queries	Abderrazak Daoudi, Younes Mechqrane, Christian Bessiere, Nadjib Lazaar, El Houssine Bouyakhf	In this paper, we propose Predict&Ask, an algorithm based on the prediction of missing constraints in the partial network learned so far.
102	Combining the k-CNF and XOR Phase-Transitions	Jeffrey M. Dudek, Kuldeep S. Meel, Moshe Y. Vardi	In this paper, we present the first study of the satisfiability of random k-CNF-XOR formulas.
103	Linear Arithmetic Satisfiability via Strategy Improvement	Azadeh Farzan, Zachary Kincaid	This article presents a novel decision procedure for LRA that leverages SMT solvers for the ground fragment of LRA, but avoids explicit quantifier elimination.
104	Constraint Detection in Natural Language Problem Descriptions	Zeynep Kiziltan, Marco Lippi, Paolo Torroni	This work aims to alleviate this issue by proposing a method for detecting constraints in natural language problem descriptions using a structured-output classifier. To evaluate the method, we develop an original annotated corpus which gathers 110 problem descriptions from several resources.
105	Improving Model Counting by Leveraging Definability	Jean-Marie Lagniez, Emmanuel Lonca, Pierre Marquis	We present a new preprocessing technique for propositional model counting.
106	Static Symmetry Breaking with the Reflex Ordering	Jimmy H. M. Lee, Zichen Zhu	We propose the ReflexLeader method, which is a variant of LexLeader using the reflex ordering instead, and give conditions when ReflexLeader is safe to combine with the Precedence and multiset ordering constraints.
107	A Clause Tableau Calculus for MaxSAT	Chu-Min Li, Felip Manyà, Joan Ramon Soler	We define a clause tableau calculus for MaxSAT, prove its soundness and completeness, and describe a tableau-based algorithm for MaxSAT.
108	Optimizing Molecular Cloning of Multiple Plasmids	Thierry Petit, Lolita Petit	In this paper, we model the Plasmid Cloning Problem in constraint programing, in order to optimize the construction of plasmids.
109	Controllable Procedural Content Generation via Constrained Multi-Dimensional Markov Chain Sampling	Sam Snodgrass, Santiago Ontañón	Statistical models, such as Markov chains, have recently started to be studied for the purpose of Procedural Content Generation (PCG).
110	Optimizing Simple Tabular Reduction with a Bitwise Representation	Ruiwei Wang, Wei Xia, Roland H. C. Yap, Zhanshan Li	We propose STRbit, a GAC algorithm, based on simple tabular reduction (STR) using an efficient bit vector support data structure.
111	Mutual Influence Potential Networks: Enabling Information Sharing in Loosely-Coupled Extended-Duration Teamwork	Ofra Amir, Barbara J. Grosz, Krzysztof Z. Gajos	This paper formalizes a new multi-agent systems problem, Information Sharing in Loosely-Coupled Extended-Duration Teamwork (ISLET).
112	Interactive Teaching Strategies for Agent Training	Ofra Amir, Ece Kamar, Andrey Kolobov, Barbara J. Grosz	We propose strategies for a teacher and a student to jointly identify advising opportunities so that the teacher is not required to constantly monitor the student.
113	Planning with Task-Oriented Knowledge Acquisition for a Service Robot	Kai Chen, Fangkai Yang, Xiaoping Chen	We propose a framework for a service robot to behave intelligently in domains that contain incomplete information, underspecified goals and dynamic change.
114	A Polynomial Time Optimal Algorithm for Robot-Human Search under Uncertainty	Shaofei Chen, Tim Baarslag, Dengji Zhao, Jing Chen, Lincheng Shen	We show that this search problem is polynomially solvable with a novel integration of the human help, which has not been studied in the literature before.
115	Apprenticeship Scheduling: Learning to Schedule from Human Experts	Matthew Gombolay, Reed Jensen, Jessica Stigile, Sung-Hyun Son, Julie Shah	We propose a new approach for capturing domain-expert heuristics through a pairwise ranking formulation.
116	DeepSchema: Automatic Schema Acquisition from Wearable Sensor Data in Restaurant Situations	Eun-Sol Kim, Kyoung-Woon On, Byoung-Tak Zhang	Here we suggest a hierarchical event network to build the hierarchical schemas and describe a novel machine learning method to learn the network from the data. For the experiments, we collected sensory data using multiple wearable devices in restaurant situations.
117	Interactive Martingale Boosting	Ashish Kulkarni, Pushpak Burange, Ganesh Ramakrishnan	We present an approach and a system that explores the application of interactive machine learning to a branching program-based boosting algorithm- Martingale Boosting.
118	Fear and Hope Emerge from Anticipation in Model-Based Reinforcement Learning	Thomas Moerland, Joost Broekens, Catholijn Jonker	This paper connects both challenges, by studying models of emotion generation in sequential decision-making agents.
119	Predictive Models of Malicious Behavior in Human Negotiations	Zahra Nazari, Jonathan Gratch	Previous research proposed a solution to this game but this required restrictive assumptions that might render it inapplicable to real-world settings.
120	Verbalization: Narration of Autonomous Robot Experience	Stephanie Rosenthal, Sai P. Selvaraj, Manuela Veloso	In this work, we address the generation of narrations of autonomous mobile robot navigation experiences.
121	Interactive Scheduling of Appliance Usage in the Home	Ngoc Cuong Truong, Tim Baarslag, Sarvapali D. Ramchurn, Long Tran-Thanh	To tackle this problem we propose iDR, an interactive system for generating personalized appliance usage scheduling recommendations that maximize savings and convenience with minimal intrusiveness.
122	Polynomial Datalog Rewritings for Expressive Description Logics with Closed Predicates	Shqiponja Ahmetaj, Magdalena Ortiz, Mantas Simkus	We consider instance queries mediated by an ontology expressed in the expressive DL ALCHIO with closed predicates.
123	Completion of Disjunctive Logic Programs	Mario Alviano, Carmine Dodaro	A different approach is proposed in this paper: Clark’s completion is extended to disjunctive programs without the need of intermediate program rewritings such as the shift.As in the non-disjunctive case, the new completion is linear in size, and discards unsupported models via unit resolution.
124	Query Answering with Transitive and Linear-Ordered Data	Antoine Amarilli, Michael Benedikt, Pierre Bourhis, Michael Vanden Boom	We consider entailment problems involving powerful constraint languages such as guarded existential rules, in which additional semantic restrictions are put on a set of distinguished relations.
125	Evaluation of Arguments from Support Relations: Axioms and Semantics	Leila Amgoud, Jonathan Ben-Naim	This paper focuses on argumentation graphs whose nodes are arguments and edges represent supports, thus positive relations, between arguments.
126	Incomplete Causal Laws in the Situation Calculus Using Free Fluents	Marcelo Arenas, Jorge A. Baier, Juan S. Navarro, Sebastian Sardina	We propose a simple relaxation of Reiter’s basic action theories, based on fluents without successor state axioms, that accommodates incompleteness beyond the initial database.
127	On the Relationship between P-log and LP^MLN	Evgenii Balai, Michael Gelfond	This work sheds light on the different ways to treat inconsistency in both languages.
128	Online Agent Supervision in the Situation Calculus	Bita Banihashemi, Giuseppe De Giacomo, Yves Lespérance	In this work, we investigate supervision of an agent that may acquire new knowledge about her environment during execution, for example, by sensing.
129	Equivalent Stream Reasoning Programs	Harald Beck, Minh Dao-Tran, Thomas Eiter	We show how a practically relevant fragment can be alternatively captured usingHere-and-There models, yielding an extension of equilibrium semantics of ASP to this class of programs.
130	The Inconsistency in Gödel’s Ontological Argument: A Success Story for AI in Metaphysics	Christoph Benzmüller, Bruno Woltzenlogel Paleo	This paper discusses the discovery of the inconsistency in Gödel’s ontological argument as a success story for artificial intelligence.
131	Ontology-Mediated Queries Distributing over Components	Gerald Berger, Andreas Pieris	For each such class, we syntactically characterize its fragment that distributes over components, and we study the problem of deciding whether a query distributes over components.
132	Leviathan: A New LTL Satisfiability Checking Tool Based on a One-Pass Tree-Shaped Tableau	Matteo Bertello, Nicola Gigante, Angelo Montanari, Mark Reynolds	The paper presents Leviathan, an LTL satisfiability checking tool based on a novel one-pass, tree-like tableau system, which is way simpler than existing solutions.
133	Query-Driven Repairing of Inconsistent DL-Lite Knowledge Bases	Meghyn Bienvenu, Camille Bourgaux, François Goasdoué	After formalizing this problem and introducing different notions of optimality, we investigate the computational complexity of reasoning about optimal repair plans and propose interactive algorithms for computing such plans.
134	First Order-Rewritability and Containment of Conjunctive Queries in Horn Description Logics	Meghyn Bienvenu, Peter Hansen, Carsten Lutz, Frank Wolter	We study FO-rewritability of conjunctive queries in the presence of ontologies formulated in a description logic between EL and Horn-SHIF, along with related query containment problems.
135	Automated Synthesis of Timed Failure Propagation Graphs	Benjamin Bittner, Marco Bozzano, Alessandro Cimatti	In this paper we present a technique to automate the construction of TFPGs.
136	ASP for Anytime Dynamic Programming on Tree Decompositions	Bernhard Bliem, Benjamin Kaufmann, Torsten Schaub, Stefan Woltran	In this paper, we present a novel ASP-based system allowing for "lazy" DP, which utilizes recent multi-shot ASP technology.
137	Extending the Harper Identity to Iterated Belief Change	Richard Booth, Jake Chandler	In this paper we extend the Harper Identity from single-step change to define iterated contraction in terms of iterated revision.
138	Preferential Query Answering over the Semantic Web with Possibilistic Networks	Stefan Borgwardt, Bettina Fazzinga, Thomas Lukasiewicz, Akanksha Shrivastava, Oana Tifrea-Marciuska	In this paper, we explore how ontological knowledge expressed via existential rules can be combined with possibilistic networks (i) to represent qualitative preferences along with domain knowledge, and (ii) to realize preference-based answering of conjunctive queries (CQs).
139	Query-Based Entailment and Inseparability for ALC Ontologies	Elena Botoeva, Carsten Lutz, Vladislav Ryzhikov, Frank Wolter, Michael Zakharyaschev	We investigate the problem whether two ALC knowledge bases are indistinguishable by queries over a given vocabulary.
140	Knowledge Compilation Meets Communication Complexity	Simone Bova, Florent Capelli, Stefan Mengel, Friedrich Slivovsky	In this paper, we introduce a general technique for obtaining lower bounds on Decomposable Negation Normal Form (DNNFs), one of the most widely studied and succinct representation languages, by relating the size of DNNFs to multi-partition communication complexity.
141	An ASP Semantics for Default Reasoning with Constraints	Pedro Cabalar, Roland Kaminski, Max Ostrowski, Torsten Schaub	We introduce the logic of Here-and-There with Constraints in order to capture constraint theories in the non-monotonic setting known from Answer Set Programming (ASP).
142	Plan Synthesis for Knowledge and Action Bases	Diego Calvanese, Marco Montali, Fabio Patrizi, Michele Stawowy	We study plan synthesis for a variant of Knowledge and Action Bases (KABs), a rich, dynamic framework, where states are description logic (DL) knowledge bases (KBs) whose extensional part is manipulated by actions that possibly introduce new objects from an infinite domain.
143	On the Impact of Modal Depth in Epistemic Planning	Tristan Charrier, Bastien Maubert, Francçois Schwarzentruber	In this work we bring two new pieces to the picture.
144	Imperfect-Information Games and Generalized Planning	Giuseppe De Giacomo, Aniello Murano, Sasha Rubin, Antonio Di Stasio	By building on work on two-player (non-probabilistic) games with imperfect information in the Formal Methods literature, we devise a general technique, generalizing the belief-state construction, to remove partial observability.
145	LTL_f and LDL_f Synthesis under Partial Observability	Giuseppe De Giacomo, Moshe Y. Vardi	In this paper, we study synthesis under partial observability for logical specifications over finite traces expressed in LTLf/LDLf.
146	Investigating the Relationship between Argumentation Semantics via Signatures	Paul E. Dunne, Christof Spanring, Thomas Linsbichler, Stefan Woltran	We investigate in total nine argumentation semantics and give a nearly complete landscape of exact characterizations.
147	Exploiting Partial Assignments for Efficient Evaluation of Answer Set Programs with External Source Access	Thomas Eiter, Tobias Kaminski, Christoph Redl, Antonius Weinzierl	In this work, we extend the evaluation principles of external atoms to partial assignments, lift nogood learning to this setting, and introduce a variant of nogood minimization.
148	Forgetting in Multi-Agent Modal Logics	Liangda Fang, Yongmei Liu, Hans van Ditmarsch	In this paper, we study forgetting in multi-agent modal logics.
149	Trend-Based Prediction of Spatial Change	Xiaoyu Ge, Jae Hee Lee, Jochen Renz, Peng Zhang	In this paper, we present a prediction approach that overcomes the aforementioned problem by using a more general model and by analysing the trend of the spatial changes.
150	Towards Fast Algorithms for the Preference Consistency Problem Based on Hierarchical Models	Anne-Marie George, Nic Wilson, Barry O’Sullivan	In this paper, we construct and compare algorithmic approaches to solve the Preference Consistency Problem for preference statements based on hierarchical models.
151	Querying Data Graphs with Arithmetical Regular Expressions	Maciej Graboń, Jakub Michaliszyn, Jan Otop, Piotr Wieczorek	We propose a query language LARE for graphs whose edges are labelled by a finite alphabet and nodes store unbounded data values.
152	On Consensus Extraction	éric Grégoire, Sébastien Konieczny, Jean Marie Lagniez	In this work, we define consensus operators as functions that deliver parts of the set-theoretical union of the information sources (inpropositional logic) to be reconciled, such that no source is logically contradicted.
153	Temporalized EL Ontologies for Accessing Temporal Data: Complexity of Atomic Queries	Víctor Gutiérrez-Basulto, Jean Christoph Jung, Roman Kontchakov	Our aim is to establish a clear computational complexity landscape for the atomic query answering problem, in terms of both data and combined complexity.
154	Distributing Knowledge into Simple Bases	Adrian Haret, Jean-Guy Mailly, Stefan Woltran	In this paper we propose the concept of belief distribution, which can be understood as the reverse task of merging.
155	Epistemic Boolean Games Based on a Logic of Visibility and Control	Andreas Herzig, Emiliano Lorini, Faustine Maffre, Francois Schwarzentruber	We analyse epistemic boolean games ina computationally grounded dynamic epistemic logic.
156	Normative Multiagent Systems: The Dynamic Generalization	Xiaowei Huang, Ji Ruan, Qingliang Chen, Kaile Su	In this paper, we propose a dynamic normative system to enable the reasoning of the changes of norms under different circumstances, which cannot be done in the existing static normative systems.
157	Eliminating Disjunctions in Answer Set Programming by Restricted Unfolding	Jianmin Ji, Hai Wan, Kewen Wang, Zhe Wang, Chuhan Zhang, Jiangtao Xu	In this paper, we provide an answer-set-preserving rewriting of a general disjunctive program to a normal program by first applying the unfolding transformation on atoms that prevent the program from being head-cycle-free, then shifting the resulting program.
158	Epistemic GDL: A Logic for Representing and Reasoning about Imperfect Information Games	Guifei Jiang, Dongmo Zhang, Laurent Perrussel, Heng Zhang	This paper proposes a logical framework for representing and reasoning about imperfect information games.
159	Question Answering via Integer Programming over Semi-Structured Knowledge	Daniel Khashabi, Tushar Khot, Ashish Sabharwal, Peter Clark, Oren Etzioni, Dan Roth	We propose a structured inference system for this task, formulated as an Integer Linear Program (ILP), that answers natural language questions using a semi-structured knowledge base derived from text, including questions requiring multi-step inference and a combination of multiple facts.
160	Conservative Rewritability of Description Logic TBoxes	Boris Konev, Carsten Lutz, Frank Wolter, Michael Zakharyaschev	We investigate the problem of conservative rewritability of a TBox T in a description logic L into a TBox T’ in a weaker description logic L’.
161	Temporal and Spatial OBDA with Many-Dimensional Halpern-Shoham Logic	Roman Kontchakov, Laura Pandolfo, Luca Pulina, Vladislav Ryzhikov, Michael Zakharyaschev	We present experimental results showing the expressivity and efficiency of datalogHS on historical data.
162	Learning Possibilistic Logic Theories from Default Rules	Ondřej Kuželka, Jesse Davis, Steven Schockaert	We introduce a setting for learning possibilistic logic theories from defaults of the form "if alpha then typically beta".
163	Answering Metaqueries over Hi (OWL 2 QL) Ontologies	Maurizio Lenzerini, Lorenzo Lepore, Antonella Poggi	In this paper we investigate the problem of answering metaqueries in Hi(OWL 2 QL), which are unions of conjunctive queries with both ABox and TBox atoms.
164	Constraint Answer Set Programming versus Satisfiability Modulo Theories	Yuliya Lierler, Benjamin Susman	In this paper, we make the link between these two areas precise.
165	Exploring the Context of Locations for Personalized Location Recommendations	Xin Liu, Yong Liu, Xiaoli Li	In this paper, by leveraging the Skip-gram model, we learn the latent representation for a location to capture the influence of its context.
166	A Decision Procedure for a Fragment of Linear Time Mu-Calculus	Yao Liu, Zhenhua Duan, Cong Tian	In this paper, we study an expressive fragment, namely Gmu, of linear time mu-calculus as a high-level goal specification language.
167	Efficient Path Consistency Algorithm for Large Qualitative Constraint Networks	Zhiguo Long, Michael Sioutis, Sanjiang Li	We propose a new algorithm called DPC+ to enforce partial path consistency (PPC) on qualitative constraint networks.
168	Belief Update for Proper Epistemic Knowledge Bases	Tim Miller, Christian Muise	In this paper, we present a belief update mechanism for PEKBs that ensures the knowledge base remains consistent when new beliefs are added.
169	Optimal Status Enforcement in Abstract Argumentation	Andreas Niskanen, Johannes P. Wallner, Matti Järvisalo	We present complexity results and algorithms for optimal status enforcement in abstract argumentation.
170	Efficient Representations for the Modal Logic S5	Alexandre Niveau, Bruno Zanuttini	We investigate efficient representations of subjective formulas in the modal logic of knowledge, S5, and more generally of sets of sets of propositional assignments.
171	Expressivity of Datalog Variants — Completing the Picture	Sebastian Rudolph, Michaël Thomazo	This paper solves the remaining open questions needed to arrive at a complete picture regarding the interrelationships between the class of homomorphism-closed queries and the query classes related to the four versions of Datalog.
172	Is Promoting Beliefs Useful to Make Them Accepted in Networks of Agents?	Nicolas Schwind, Katsumi Inoue, Gauvain Bourgne, Sébastien Konieczny, Pierre Marquis	In this paper, we investigate the extent to which BRGs satisfy some monotonicity properties, i.e., whether promoting some desired piece of belief to a given set of agents is actually always useful for making it accepted by all of them.
173	Normative Practical Reasoning via Argumentation and Dialogue	Zohreh Shams, Marina De Vos, Nir Oren, Julian Padget	We propose a solution in which a normative planning problem serves as the basis for a practical reasoning approach based on argumentation.
174	Efficient Sequential Model-Based Fault-Localization with Partial Diagnoses	Kostyantyn Shchekotykhin, Thomas Schmitz, Dietmar Jannach	In this paper we propose a sound and complete sequential diagnosis approach which does not require any information about the structure of the diagnosed system.
175	Object-Relational Queries over CFDI_nc Knowledge Bases: OBDA for the SQL-Literate	Jason St. Jacques, David Toman, Grant Weddell	Our main results present efficient algorithms that allow computation ofcertain answers with respect to CFDnc-forall nowledge bases, facilitating direct access to a pre-existing row-basedrelational encoding of the data without any need for mappings to triple-based representations.
176	Diagnosability of Discrete-Event Systems with Uncertain Observations	Xingyu Su, Marina Zanella, Alban Grastien	The present paper provides an answer to this question when the observation is temporally or logically uncertain, that is, when the order of the observed events or their (discrete) values are partially unknown.
177	Sampling-Based Belief Revision	Michael Thielscher	In this paper, we examine the logical foundations of sampling-based belief revision.
178	Efficient Query Answering over Expressive Inconsistent Description Logics	Eleni Tsalapati, Giorgos Stoilos, Giorgos Stamou, George Koletsos	In the current paper we present a framework for scalable query answering under both the IAR and ICAR semantics, which is based on highly efficient data saturation systems.
179	Distributed Autoepistemic Logic and its Application to Access Control	Pieter Van Hertum, Marcos Cramer, Bart Bogaerts, Marc Denecker	In this paper we define and study an extension of autoepistemic logic (AEL) called distributed autoepistemic logic (dAEL) with multiple agents that have full introspection in their own knowledge as well as in that of others.
180	Text-Enhanced Representation Learning for Knowledge Graph	Zhigang Wang, Juanzi Li	In this paper, we propose a novel knowledge graph representation learning method by taking advantage of the rich context information in a text corpus.
181	On the Representation and Embedding of Knowledge Bases beyond Binary Relations	Jianfeng Wen, Jianxin Li, Yongyi Mao, Shini Chen, Richong Zhang	We advocate a novel modelling framework, which models multi-fold relations directly using this canonical representation.Using this framework, the existing TransH model is generalized to a new model, m-TransH.
182	Connecting Qualitative Spatial and Temporal Representations by Propositional Closure	Diedrich Wolter, Jae Hee Lee	In this paper we consider propositional closures of qualitative constraints which enable progress with respect to the longstanding challenge.
183	From One Point to a Manifold: Knowledge Graph Embedding for Precise Link Prediction	Han Xiao, Minlie Huang, Xiaoyan Zhu	As precise link prediction is critical, we propose a manifold-based embedding principle (ManifoldE) which could be treated as a well-posed algebraic system that expands the position of golden triples from one point in current models to a manifold in ours.
184	Strategy Representation and Reasoning for Incomplete Information Concurrent Games in the Situation Calculus	Liping Xiong, Yongmei Liu	In this paper, by a simple extension of a variant of multi-agent epistemic situation calculus with a strategy sort, we develop a general framework for strategy representation and reasoning for incomplete information concurrent games.
185	Expressive Completeness of Existential Rule Languages for Ontology-Based Query Answering	Heng Zhang, Yan Zhang, Jia-Huai You	In this paper, we prove that disjunctive embedded dependencies exactly capture the class of recursively enumerable ontologies in Ontology-based Conjunctive Query Answering (OCQA).
186	A Characterization of the Semantics of Logic Programs with Aggregates	Yuanlin Zhang, Maede Rayatidamavandi	In this paper, we aim to understand these distinct semantics in a more uniform way.
187	Forgetting Concept and Role Symbols in ALCOIHμ⁺(∇, ⊓)-Ontologies	Yizheng Zhao, Renate A. Schmidt	In this paper, we present an Ackermann-based approach for forgetting of concept and role symbols in ontologies expressible in the description logic ALCOIHmu+(top,and).
188	Driver Frustration Detection from Audio and Video in the Wild	Irman Abdić, Lex Fridman, Daniel McDuff, Erik Marchi, Bryan Reimer, Björn Schuller	We present a method for detecting driver frustration from both video and audio streams captured during the driver’s interaction with an in-vehicle voice-based navigation system.
189	The Complexity of Learning Acyclic CP-Nets	Eisa Alanazi, Malek Mouhoub, Sandra Zilles	To assess the optimality of learning algorithms as well as to better understand the combinatorial structure of CP-net classes, it is helpful to calculate certain learning-theoretic information complexity parameters.
190	Change Detection in Multivariate Datastreams: Likelihood and Detectability Loss	Cesare Alippi, Giacomo Boracchi, Diego Carrera, Manuel Roveri	Despite the fact that this approach constitutes the frame of several change-detection methods, its effectiveness when data dimension scales has never been investigated, which is indeed the goal of our paper.
191	Cold-Start Recommendations for Audio News Stories Using Matrix Factorization	Ehsan Mohammady Ardehaly, Aron Culotta, Vivek Sundararaman, Alwar Narayanan	To address the first challenge, we formulate the problem as predicting the percentage of a story a user will listen to; to address the remaining challenges, we propose several matrix factorization algorithms that cluster users, n-grams, and stories simultaneously, while optimizing prediction accuracy.
192	MPMA: Mixture Probabilistic Matrix Approximation for Collaborative Filtering	Chao Chen, Dongsheng Li, Qin Lv, Junchi Yan, Stephen M. Chu, Li Shang	In this paper, a mixture probabilistic matrix approximation (MPMA) method is proposed, which unifies globally optimized user/item feature vectors (on the entire rating matrix) and locally optimized user/item feature vectors (on subsets of user/item ratings) to improve recommendation accuracy.
193	A Generalized Matching Pursuit Approach for Graph-Structured Sparsity	Feng Chen, Baojian Zhou	In this paper, we focus on sparsity-constrained optimization in cases where the cost function is a general nonlinear function and, in particular, the sparsity constraint is defined by a graph-structured sparsity model.
194	Entity Embedding-Based Anomaly Detection for Heterogeneous Categorical Events	Ting Chen, Lu-An Tang, Yizhou Sun, Zhengzhang Chen, Kai Zhang	Different from previous work, we propose a principled and unified probabilistic model APE (Anomaly detection via Probabilistic pairwise interaction and Entity embedding) that directly models the likelihood of events.
195	ADL™: A Topic Model for Discovery of Activities of Daily Living in a Smart Home	Yu Chen, Tom Diethe, Peter Flach	We present an unsupervised approach for discovery of Activities of Daily Living (ADL) in a smart home.
196	Cost-Aware Pre-Training for Multiclass Cost-Sensitive Deep Learning	Yu-An Chung, Hsuan-Tien Lin, Shao-Wen Yang	Cost-Aware Pre-Training for Multiclass Cost-Sensitive Deep Learning
197	Learning Higher-Order Logic Programs through Abstraction and Invention	Andrew Cropper, Stephen H. Muggleton	To reduce the complexity of the learned programs, and thus the search for such a program, we introduce higher-order operations involving an alternation of Abstraction and Invention.
198	A Unifying Framework for Learning Bag Labels from Generalized Multiple-Instance Data	Gary Doran, Andrew Latham, Soumya Ray	We study the problem of bag-level classification from generalized multiple-instance (GMI) data.
199	Hidden Parameter Markov Decision Processes: A Semiparametric Regression Approach for Discovering Latent Task Parametrizations	Finale Doshi-Velez, George Konidaris	We introduce the Hidden Parameter Markov Decision Process (HiP-MDP), a framework that parametrizes a family of related dynamical systems with a low-dimensional set of latent factors, and introduce a semiparametric regression approach for learning its structure from data.
200	EBEK: Exemplar-Based Kernel Preserving Embedding	Ahmed Elbagoury, Rania Ibrahim, Mohamed S. Kamel, Fakhri Karray	In this work, a new embedding technique is proposed to mitigate the previous problems by projecting the data to a space described by few points (i.e, exemplars) which preserves the relations between the data points.
201	Version Space Reduction Based on Ensembles of Dissimilar Balanced Perceptrons	Karen Braga Enes, Saulo Moraes Villela, Raul Fonseca Neto	In this paper we present the Version Space Reduction Machine (VSRM), a new method that obtains an approximation of the center of mass.
202	Robust Domain Generalisation by Enforcing Distribution Invariance	Sarah M. Erfani, Mahsa Baktashmotlagh, Masud Moshtaghi, Vinh Nguyen, Christopher Leckie, James Bailey, Kotagiri Ramamohanarao	We propose Elliptical Summary Randomisation (ESRand), an efficient domain generalisation approach that comprises of a randomized kernel and elliptical data summarisation.
203	Copula Mixed-Membership Stochastic Blockmodel	Xuhui Fan, Richard Yi Da Xu, Longbing Cao	To expand MMSB’s ability to model such dependent relationships, a new framework – a Copula Mixed-Membership Stochastic Blockmodel – is introduced in this paper for modeling intra-group correlations, namely an individual Copula function jointly models the membership pairs of those nodes within the group of interest.
204	DrMAD: Distilling Reverse-Mode Automatic Differentiation for Optimizing Hyperparameters of Deep Neural Networks	Jie Fu, Hongyin Luo, Jiashi Feng, Kian Hsiang Low, Tat-Seng Chua	In this work we propose a simple but effective method, DrMAD, to distill the knowledge of the forward pass into a shortcut path, through which we approximately reverse the training trajectory.
205	A Robust Convex Formulation for Ensemble Clustering	Junning Gao, Makoto Yamada, Samuel Kaski, Hiroshi Mamitsuka, Shanfeng Zhu	We formulate ensemble clustering as a regularization problem over nuclear norm and cluster-wise group norm, and present an efficient optimization algorithm, which we call Robust Convex Ensemble Clustering (RCEC).
206	Semi-Data-Driven Network Coarsening	Li Gao, Jia Wu, Hong Yang, Zhi Qiao, Chuan Zhou, Yue Hu	In this paper, we present a new semi-data-driven network coarsening model to learn coarsened networks by embedding both static network structure data and dynamic network information spreading data.
207	Constrained Local Latent Variable Discovery	Tian Gao, Qiang Ji	In this work, we propose a method to identify local latent variables and to determine their structural relations with the observed variables.
208	Knowledge-Based Sequence Mining with ASP	Martin Gebser, Thomas Guyet, René Quiniou, Javier Romero, Torsten Schaub	We introduce a framework for knowledge-based sequence mining, based on Answer Set Programming (ASP).
209	Incremental Truncated LSTD	Clement Gehring, Yangchen Pan, Martha White	In this work, we develop an efficient incremental low-rank LSTD(λ) algorithm that progresses towards the goal of better balancing computation and sample efficiency.
210	A Distributed and Scalable Machine Learning Approach for Big Data	Hongliang Guo, Jie Zhang	To address the big data related challenges, we first partition the data along its feature space, and apply the parallel block coordinate descent algorithm for distributed computation; then, we continue to partition the data along the sample space, and propose a novel matrix decomposition and combination approach for distributed processing.
211	Deep Learning for Reward Design to Improve Monte Carlo Tree Search in ATARI Games	Xiaoxiao Guo, Satinder Singh, Richard Lewis, Honglak Lee	We present an adaptation of PGRD (policy-gradient for reward-design) for learning a reward-bonus function to improve UCT (a MCTS algorithm).
212	Semi-Supervised Active Learning with Cross-Class Sample Transfer	Yuchen Guo, Guiguang Ding, Yue Gao, Jianmin Wang	In this paper, we consider a more practical and challenging setting where the source domain and the target domain have different but related classes.
213	Deep, Convolutional, and Recurrent Models for Human Activity Recognition Using Wearables	Nils Y. Hammerla, Shane Halloran, Thomas Plötz	In this paper we rigorously explore deep, convolutional, and recurrent approaches across three representative datasets that contain movement data captured with wearable sensors.
214	Incorporating External Knowledge into Crowd Intelligence for More Specific Knowledge Acquisition	Tao Han, Hailong Sun, Yangqiu Song, Yili Fang, Xudong Liu	In this paper, we consider the problem of collecting as specific as possible knowledge via crowdsourcing.
215	Partially Supervised Graph Embedding for Positive Unlabelled Feature Selection	Yufei Han, Yun Shen	To leverage the partially observed positive class information, we propose to encode the weakly supervised information in PU learning tasks into pairwise constraints between training instances.
216	Online Bayesian Max-Margin Subspace Multi-View Learning	Jia He, Changying Du, Fuzhen Zhuang, Xin Yin, Qing He, Guoping Long	In this paper, we propose an online Bayesian multi-view learning algorithm to learn predictive subspace with max-margin principle.
217	Tight Policy Regret Bounds for Improving and Decaying Bandits	Hoda Heidari, Michael Kearns, Aaron Roth	For the decreasing case, we present a simple greedy approach and show that the policy regret of this algorithm is constant and upper bounded by the number of arms.
218	Bayesian Reinforcement Learning with Behavioral Feedback	Teakgyu Hong, Jongmin Lee, Kee-Eung Kim, Pedro A. Ortega, Daniel Lee	In this paper, we present a Bayesian approach to this extended reinforcement learning setting.
219	Grounding Topic Models with Knowledge Bases	Zhiting Hu, Gang Luo, Mrinmaya Sachan, Eric Xing, Zaiqing Nie	In this paper, we propose a structured topic representation based on an entity taxonomy from a knowledge base.
220	Class-Wise Supervised Hashing with Label Embedding and Active Bits	Long-Kai Huang, Sinno Jialin Pan	To overcome this limitation, we propose a class-wise supervised hashing method that trains a model based on a class-pairwise similarity matrix, whose size is much smaller than the instance-pairwise similarity matrix in many applications.
221	Transfer Learning with Active Queries from Source Domain	Sheng-Jun Huang, Songcan Chen	To solve this practical yet rarely studied problem, in this paper, we jointly perform transfer learning and active learning by querying the most valuable information from the source domain.
222	Learning Stable Linear Dynamical Systems with the Weighted Least Square Method	Wenbing Huang, Lele Cao, Fuchun Sun, Deli Zhao, Huaping Liu, Shanshan Yu	In this paper, we propose a novel approach for learning stable systems by enforcing stability directly on the least-square solutions.
223	Learning Unified Features from Natural and Programming Languages for Locating Buggy Source Code	Xuan Huo, Ming Li, Zhi-Hua Zhou	In this paper, we propose a novel convolutional neural network NP-CNN, which leverages both lexical and program structure information to learn unified features from natural language and source code in programming language for automatically locating the potential buggy source code according to bug report.
224	Change Detection Using Directional Statistics	Tsuyoshi Idé, Dzung T. Phan, Jayant Kalagnanam	To capture major patterns, we introduce a regularized maximum likelihood equation for the von Mises-Fisher distribution, which simultaneously learns directional statistics and sample weights to filter out unwanted samples contaminated by the noise.
225	Using Task Features for Zero-Shot Knowledge Transfer in Lifelong Learning	David Isele, Mohammad Rostami, Eric Eaton	To reduce this burden, we develop a lifelong reinforcement learning method based on coupled dictionary learning that incorporates high-level task descriptors to model the inter-task relationships.
226	Multi-Label Informed Feature Selection	Ling Jian, Jundong Li, Kai Shu, Huan Liu	In this paper, we propose a novel multi-label informed feature selection framework MIFS, which exploits label correlations to select discriminative features across multiple labels.
227	Robust Out-of-Sample Data Recovery	Bo Jiang, Chris Ding, Bin Luo	In this paper, we propose a new robust out-of-sample data recovery (ROSR) model for trace norm based regularization methods.
228	On Structural Properties of MDPs that Bound Loss Due to Shallow Planning	Nan Jiang, Satinder Singh, Ambuj Tewari	In this paper, we consider planning with accurate models and investigate structural properties of MDPs that bound the loss incurred by using smaller than specified planning horizons.
229	Constructing Abstraction Hierarchies Using a Skill-Symbol Loop	George Konidaris	We describe a framework for building abstraction hierarchies whereby an agent alternates skill- and representation-construction phases to construct a sequence of increasingly abstract Markov decision processes.
230	Bounds for Learning from Evolutionary-Related Data in the Realizable Case	Ondřej Kuželka, Yuyi Wang, Jan Ramon	This paper deals with the generalization ability of classifiers trained from non-iid evolutionary-related data in which all training and testing examples correspond to leaves of a phylogenetic tree.
231	Learning Multi-Step Predictive State Representations	Lucas Langer, Borja Balle, Doina Precup	In this paper we introduce the multi-step predictive state representation (M-PSR) and an associated learning algorithm that finds and leverages frequent patterns of observations at multiple scales in dynamical systems with discrete observations.
232	Dual-Memory Deep Learning Architectures for Lifelong Learning of Everyday Human Behaviors	Sang-Woo Lee, Chung-Yeon Lee, Dong Hyun Kwak, Jiwon Kim, Jeonghee Kim, Byoung-Tak Zhang	Here we propose a dual memory architecture that processes slow-changing global patterns as well as keeps track of fast-changing local behaviors over a lifetime.
233	Predicting Personal Traits from Facial Images Using Convolutional Neural Networks Augmented with Facial Landmark Information	Yoad Lewenberg, Yoram Bachrach, Sukrit Shankar, Antonio Criminisi	To further improve performance, we propose a novel approach that incorporates facial landmark information for input images as an additional channel, helping the CNN learn better attribute-specific features so that the landmarks across various training images hold correspondence.
234	A Relaxed Ranking-Based Factor Model for Recommender System from Implicit Feedback	Huayu Li, Richang Hong, Defu Lian, Zhiang Wu, Meng Wang, Yong Ge	To this end, we propose a Relaxed Ranking-based Factor Model, RRFM, to relax pairwise ranking into a SVM-like task, where positive and negative feedbacks are separated by the soft boundaries, and their non-separate property is employed to capture the characteristic of unobserved data.
235	Adversarial Sequence Tagging	Jia Li, Kaiser Asif, Hong Wang, Brian D. Ziebart, Tanya Berger-Wolf	We present adversarial sequence tagging, a consistent structured prediction framework for minimizing Hamming loss by pessimistically viewing uncertainty.
236	Joint Feature Selection and Structure Preservation for Domain Adaptation	Jingjing Li, Jidong Zhao, Ke Lu	This paper proposes a novel approach, named joint Feature Selection and Structure Preservation (FSSP), for unsupervised domain adaptation.
237	Multiple Kernel Clustering with Local Kernel Alignment Maximization	Miaomiao Li, Xinwang Liu, Lei Wang, Yong Dou, Jianping Yin, En Zhu	To address these issues, this paper proposes a novel MKC algorithm with a "local" kernel alignment, which only requires that the similarity of a sample to its k-nearest neighbours be aligned with the ideal similarity matrix.
238	Feature Learning Based Deep Supervised Hashing with Pairwise Labels	Wu-Jun Li, Sheng Wang, Wang-Cheng Kang	In this paper, we propose a novel deep hashing method, called deep pairwise-supervised hashing (DPSH), to perform simultaneous feature learning and hash-code learning for applications with pairwise labels.
239	Multi-View Learning with Limited and Noisy Tagging	Yingming Li, Ming Yang, Zenglin Xu, Zhongfei (Mark) Zhang	In this paper, we investigate this challenging problem of learning with limited and noisy tagging and propose a discriminative model, called MSMC, that exploits both labeled and unlabeled data through a semi-parametric regularization and takes advantage of the multi-label space consistency into the optimization.
240	Graph Quality Judgement: A Large Margin Expedition	Yu-Feng Li, Shao-Bo Wang, Zhi-Hua Zhou	In this paper we propose a large margin separation method ‘Lead’ for safe GSSL.
241	Sparse Bayesian Content-Aware Collaborative Filtering for Implicit Feedback	Defu Lian, Yong Ge, Nicholas Jing Yuan, Xing Xie, Hui Xiong	To this end, we propose a sparse Bayesian content-aware collaborative filtering framework especially for implicit feedback, and develop a scalable optimization algorithm to jointly learn latent factors and hyperparameters.
242	Group-Invariant Cross-Modal Subspace Learning	Jian Liang, Ran He, Zhenan Sun, Tieniu Tan	Apart from lower pairwise correspondences that force the data from one pair to be close to each other, we propose a novel concept, referred as groupwise correspondences, supposing that each paired heterogeneous data are from an identical latent group.
243	Learning to Detect Concepts from Webly-Labeled Video Data	Junwei Liang, Lu Jiang, Deyu Meng, Alexander Hauptmann	To leverage the noisy web labels, we propose a novel method called WEbly-Labeled Learning (WELL).
244	Towards Convolutional Neural Networks Compression via Global Error Reconstruction	Shaohui Lin, Rongrong Ji, Xiaowei Guo, Xuelong Li	In this paper, we target at compressing CNN models to an extreme without significantly losing their discriminability.
245	Efficient k-Support-Norm Regularized Minimization via Fully Corrective Frank-Wolfe Method	Bo Liu, Xiao-Tong Yuan, Shaoting Zhang, Qingshan Liu, Dimitris N. Metaxas	In this paper, we reformulate the k-support-norm regularized formulation into an identical constrained formulation and propose a fully corrective Frank-Wolfe algorithm to minimize the constrained model.
246	Supervised Matrix Factorization for Cross-Modality Hashing	Hong Liu, Rongrong Ji, Yongjian Wu, Gang Hua	In this paper, we propose a novel cross-modality hashing algorithm termed Supervised Matrix Factorization Hashing (SMFH) which tackles the multi-modal hashing problem with a collective non-negative matrix factorization across the different modalities.
247	Aligning Users across Social Networks Using Network Embedding	Li Liu, William K. Cheung, Xin Li, Lejian Liao	In this paper, we adopt the representation learning approach to align users across multiple social networks where the social structures of the users are exploited.
248	Transductive Optimization of Top k Precision	Li-Ping Liu, Thomas G. Dietterich, Nan Li, Zhi-Hua Zhou	This paper shows the importance of incorporating the knowledge of k into the learning process and introduces a new approach, Transductive Top K (TTK), that seeks to minimize the hinge loss over all training instances under the constraint that exactly k test instances are predicted as positive.
249	Natural Supervised Hashing	Qi Liu, Hongtao Lu	In this paper, we propose a very straightforward supervised hashing algorithm and demonstrate its superiority over several state-of-the-art methods.
250	Linear-Time Outlier Detection via Sensitivity	Mario Lucic, Olivier Bachem, Andreas Krause	We propose a novel algorithm based on the intuition that outliers have a significant influence on the quality of divergence-based clustering solutions.
251	Avoiding Optimal Mean Robust PCA/2DPCA with Non-Greedy ℓ₁-Norm Maximization	Minnan Luo, Feiping Nie, Xiaojun Chang, Yi Yang, Alexander Hauptmann, Qinghua Zheng	Some studies consider this issue and integrate the estimation of the optimal mean into the dimension reduction objective, which leads to expensive computation.In this paper, we equivalently reformulate the maximization of variances for robust PCA, such that the optimal projection directions are learned by maximizing the sum of the projected difference between each pair of instances, rather than the difference between each instance and the mean of the data.Based on this reformulation, we propose a novel robust PCA to automatically avoid the calculation of the optimal mean based on ℓ1-norm distance.
252	On Combining Side Information and Unlabeled Data for Heterogeneous Multi-Task Metric Learning	Yong Luo, Yonggang Wen, Dacheng Tao	On Combining Side Information and Unlabeled Data for Heterogeneous Multi-Task Metric Learning
253	Multi-Grained Role Labeling Based on Multi-Modality Information for Real Customer Service Telephone Conversation	Weizhi Ma, Min Zhang, Yiqun Liu, Shaoping Ma	In this paper, we propose a four-phase framework for role labeling in real customer service telephone conversation, with the benefit of integrating multi-modality features, i.e., both low-level acoustic features and semantic-level textual features.
254	Predict Anchor Links across Social Networks via an Embedding Approach	Tong Man, Huawei Shen, Shenghua Liu, Xiaolong Jin, Xueqi Cheng	To offer a robust method, we propose a novel supervised model, called PALE, which employs network embedding with awareness of observed anchor links as supervised information to capture the major and specific structural regularities and further learns a stable cross-network mapping for predicting anchor links.
255	Efficient Bayesian Clustering for Reinforcement Learning	Travis Mandel, Yun-En Liu, Emma Brunskill, Zoran Popovic	We propose Thompson Clustering for Reinforcement Learning (TCRL), a family of Bayesian clustering algorithms for reinforcement learning that leverage structure in the state space to remain computationally efficient while controlling both exploration and generalization.
256	Soft Margin Consistency Based Scalable Multi-View Maximum Entropy Discrimination	Liang Mao, Shiliang Sun	In this paper, we propose soft margin consistency based multi-view MED (SMVMED) achieving margin consistency in a less strict way, which minimizes the relative entropy between the posteriors of two view margins.
257	Sum-Product-Max Networks for Tractable Decision Making	Mazen Melibari, Pascal Poupart, Prashant Doshi	We present a new representation called sum-product-max network (SPMN) that generalizes a sum-product network (SPN) to the class of decision-making problems and whose solution, analogous to DCs, scales linearly in the size of the network.
258	Asynchronous Accelerated Stochastic Gradient Descent	Qi Meng, Wei Chen, Jingcheng Yu, Taifeng Wang, Zhi-Ming Ma, Tie-Yan Liu	In this paper, we provide our formal answer to this question.
259	Coordinate Discrete Optimization for Efficient Cross-View Image Retrieval	Yadong Mu, Wei Liu, Cheng Deng, Zongting Lv, Xinbo Gao	As a key differentiator, our proposed method directly conducts optimization on discrete binary hash codes, rather than relaxed continuous variables as in existing cross-view hashing methods.
260	Fast Laplace Approximation for Sparse Bayesian Spike and Slab Models	Syed Abbas Z. Naqvi, Shandian Zhe, Yuan Qi, Yifan Yang, Jieping Ye	To do so, we propose a simple yet effective fast approximate Bayesian inference algorithm based on Laplace’s method.
261	Subspace Clustering via New Low-Rank Model with Discrete Group Structure Constraint	Feiping Nie, Heng Huang	We propose a new subspace clustering model to segment data which is drawn from multiple linear or affine subspaces.
262	Parameter-Free Auto-Weighted Multiple Graph Learning: A Framework for Multiview Clustering and Semi-Supervised Classification	Feiping Nie, Jing Li, Xuelong Li	In this paper, we focus on the real-world applications where the same instance can be represented by multiple heterogeneous features.
263	Gated Probabilistic Matrix Factorization: Learning Users’ Attention from Missing Values	Shohei Ohsawa, Yachiko Obara, Takayuki Osogami	We incorporate this new user model into PMF and show that the resulting method, Gated PMF (GPMF), improves the predictive accuracy by several percent on standard datasets.
264	Tri-Party Deep Network Representation	Shirui Pan, Jia Wu, Xingquan Zhu, Chengqi Zhang, Yang Wang	In this paper, we propose TriDNR, a tri-party deep network representation model, using information from three parties: node structure, node content, and node labels (if available) to jointly learn optimal node representation.
265	Outlier Detection in Complex Categorical Data by Modeling the Feature Value Couplings	Guansong Pang, Longbing Cao, Ling Chen	This paper introduces a novel unsupervised outlier detection method, namely Coupled Biased Random Walks (CBRW), for identifying outliers in categorical data with diversified frequency distributions and many noisy features.
266	Fast Learning from Distributed Datasets without Entity Matching	Giorgio Patrini, Richard Nock, Stephen Hardy, Tiberio Caetano	We present an end-to-end solution which bypasses matching entities, based on the recently introduced concept of Rademacher observations (rados).
267	Direct Sparsity Optimization Based Feature Selection for Multi-Class Classification	Hanyang Peng, Yong Fan	To solve the direct sparsity optimization problem that is non-smooth and non-convex when 0 < p < 1, we propose an efficient iterative algorithm with proved convergence by converting it to a convex and smooth optimization problem at every iteration step.
268	Deep Subspace Clustering with Sparsity Prior	Xi Peng, Shijie Xiao, Jiashi Feng, Wei-Yun Yau, Zhang Yi	In this paper, we propose a novel subspace clustering method — deeP subspAce clusteRing with sparsiTY prior (PARTY) — based on a new deep learning architecture.
269	Self-Paced Boost Learning for Classification	Te Pi, Xi Li, Zhongfei Zhang, Deyu Meng, Fei Wu, Jun Xiao, Yueting Zhuang	Motivated by simultaneously enhancing the learning effectiveness and robustness, we propose a unified framework, Self-Paced Boost Learning (SPBL).
270	Parallel Pareto Optimization for Subset Selection	Chao Qian, Jing-Cheng Shi, Yang Yu, Ke Tang, Zhi-Hua Zhou	In this paper, we propose PPOSS, a parallel version of POSS.
271	Derivative-Free Optimization of High-Dimensional Non-Convex Functions by Sequential Random Embeddings	Hong Qian, Yi-Qi Hu, Yang Yu	We characterize the properties of random embedding for this kind of problems, and propose the sequential random embeddings (SRE) to reduce the embedding gap while running optimization algorithms in the low-dimensional spaces.
272	Bridging LSTM Architecture and the Neural Dynamics during Reading	Peng Qian, Xipeng Qiu, Xuanjing Huang	Bridging LSTM Architecture and the Neural Dynamics during Reading
273	Non-Negative Matrix Factorization with Sinkhorn Distance	Wei Qian, Bin Hong, Deng Cai, Xiaofei He, Xuelong Li	In this paper, we propose a novel method that exploits knowledge in both data manifold and features correlation.
274	Dependency Clustering of Mixed Data with Gaussian Mixture Copulas	Vaibhav Rajan, Sakyajit Bhattacharya	In this paper we use Gaussian mixture copulas, to model complex dependencies beyond those captured by meta-Gaussian distributions, for clustering.
275	Trading-Off Cost of Deployment Versus Accuracy in Learning Predictive Models	Daniel P. Robinson, Suchi Saria	We propose a novel framework for designing cost-sensitive structured regularizers that is suitable for problems with complex cost dependencies.
276	Portfolio Blending via Thompson Sampling	Weiwei Shen, Jun Wang	In this paper, we present a novel online algorithm that leverages Thompson sampling into the sequential decision-making process for portfolio blending.
277	Adaptive Variance Reducing for Stochastic Gradient Descent	Zebang Shen, Hui Qian, Tengfei Zhou, Tongzhou Mu	In this paper, we propose a novel sampling scheme that explicitly computes some Adaptive Probability (AP) at each iteration.
278	Diversifying Convex Transductive Experimental Design for Active Learning	Lei Shi, Yi-Dong Shen	To this end, we proposes Diversified CTED by seamlessly incorporating a novel and effective diversity regularizer into CTED, ensuring the selected samples are diverse.
279	Improving CNN Performance with Min-Max Objective	Weiwei Shi, Yihong Gong, Jinjun Wang	In this paper, we propose a novel method to improve object recognition accuracies of convolutional neural networks (CNNs) by embedding the proposed Min-Max objective into a high layer of the models during the training process.
280	Distance-Preserving Probabilistic Embeddings with Side Information: Variational Bayesian Multidimensional Scaling Gaussian Process	Harold Soh	In this work, we seek probabilistic embeddings that faithfully represent observed relationships between objects (e.g., physical distances, preferences).
281	Fast Structural Binary Coding	Dongjin Song, Wei Liu, David A. Meyer	In this paper, we propose a novel supervised binary coding approach, namely Fast Structural Binary Coding (FSBC), to optimize the precision at the top of a Hamming distance ranking list and ensure that similar images can be returned as a whole.
282	Unsupervised Alignment of Actions in Video with Text Descriptions	Young Chol Song, Iftekhar Naim, Abdullah Al Mamun, Kaustubh Kulkarni, Parag Singla, Jiebo Luo, Daniel Gildea, Henry Kautz	This paper presents a framework that extracts higher level representations of low-level action features through hyperfeature coding from video and aligns them with language.
283	Distance Based Modeling of Interactions in Structured Regression	Ivan Stojkovic, Vladisav Jelisavcic, Veljko Milutinovic, Zoran Obradovic	We utilized a Gaussian Conditional Random Field model, where we have extended its originally proposed interaction potential to include a distance term.
284	Supervised Heterogeneous Domain Adaptation via Random Forests	Sanatan Sukhija, Narayanan C Krishnan, Gurkanwal Singh	In this paper, we present a novel supervised domain adaptation algorithm (SHDA-RF) that learns the mapping between heterogeneous features of different dimensions.
285	Learning Compact Neural Word Embeddings by Parameter Space Sharing	Jun Suzuki, Masaaki Nagata	We propose a learning framework that can provide a set of `compact’ embedding vectors for the purpose of enhancing `usability’ in actual applications.
286	A Novel Feature Matching Strategy for Large Scale Image Retrieval	Hao Tang, Hong Liu	Feature-to-feature matching is the key issue in the Bag-of-Features model.The baseline approach employs a coarse feature-to-feature matching, namely, two descriptors are assumed to match if they are assigned the same quantization index.However, this Hard Assignment strategy usually incurs undesirable low precision.To fix it, Multiple Assignment and Soft Assignment are proposed.These two methods reduce the quantization error to some extent, but there are still a lot of room for improvement.To further improve retrieval precision, in this paper, we propose a novel feature matching strategy, called local-restricted Soft Assignment (lrSA), in which a new feature matching function is introduced.The lrSA strategy is evaluated through extensive experiments on five benchmark datasets.Experiments show that the results exceed the retrieval performance of current quantization methods on these datasets.Combined with post-processing steps, we have achieved competitive results compared with the state-of-the-art methods.Overall, our strategy shows notable benefit for retrieval with large vocabularies and dataset size.
287	Learning Using Unselected Features (LUFe)	Joseph G. Taylor, Viktoriia Sharmanska, Kristian Kersting, David Weir, Novi Quadrianto	We propose that these unselected features may instead be used as an additional source of information at train time.
288	Constructive Preference Elicitation by Setwise Max-Margin Learning	Stefano Teso, Andrea Passerini, Paolo Viappiani	In this paper we propose an approach to preference elicitation that is suitable to large configuration spaces beyond the reach of existing state-of-the-art approaches.
289	Inference Machines for Nonparametric Filter Learning	Arun Venkatraman, Wen Sun, Martial Hebert, Byron Boots, J. Andrew Bagnell	In this work, we propose using inference machines to directly optimize the filtering performance.
290	Dynamic Early Stopping for Naive Bayes	Aäron Verachtert, Hendrik Blockeel, Jesse Davis	In this paper, we propose a variant of the widely used Naive Bayes (NB) learner that yields a more efficient predictive model.
291	Policy Search in Reproducing Kernel Hilbert Space	Ngo Anh Vien, Peter Englert, Marc Toussaint	In this paper, we propose to use a general framework to derive a new RKHS policy search technique.
292	Generalized Dictionary for Multitask Learning with Boosting	Boyu Wang, Joelle Pineau	In this paper, we present a new approach that combines dictionary learning with gradient boosting to achieve multitask learning with general (nonlinear) basis functions.
293	Fast Robust Non-Negative Matrix Factorization for Large-Scale Human Action Data Clustering	De Wang, Feiping Nie, Heng Huang	To address this challenge, in this paper, we propose three new NMF and NMTF models which are robust to outliers.
294	Cost-Saving Effect of Crowdsourcing Learning	Lu Wang, Zhi-Hua Zhou	In this paper, we theoretically study the cost-saving effect of crowdsourcing learning, and present an upper bound for the minimally-sufficient number of crowd labels for effective crowdsourcing learning.
295	Dealing with Multiple Classes in Online Class Imbalance Learning	Shuo Wang, Leandro L. Minku, Xin Yao	This paper studies the combined challenges posed by multi-class imbalance and online learning, and aims at a more effective and adaptive solution.
296	Coupled Marginalized Auto-Encoders for Cross-Domain Multi-View Learning	Shuyang Wang, Zhengming Ding, Yun Fu	In this paper, we propose a Coupled Marginalized Denoising Auto-encoders framework to address the cross-domain problem.
297	Learning First-Order Logic Embeddings via Matrix Factorization	William Yang Wang, William W. Cohen	In this work, we take a rather radical approach: we aim at learning continuous low-dimensional embeddings for first-order logic from scratch.
298	Constrained Preference Embedding for Item Recommendation	Xin Wang, Congfu Xu, Yunhui Guo, Hui Qian	To test our assumption, we propose two models called CPE-s and CPE-ps based on CPE for item recommendation, and show that the popular pair-wise ranking model BPR-MF can be deduced by some restrictions and variations on CPE-s.
299	Nonparametric Risk and Stability Analysis for Multi-Task Learning Problems	Xuezhi Wang, Junier B. Oliva, Jeff Schneider, Barnabás Póczos	In this paper, we study how different smoothness assumptions on task relations affect the upper bounds of algorithms proposed for these problems under different settings.
300	Iterative Views Agreement: An Iterative Low-Rank Based Structured Optimization Method to Multi-View Spectral Clustering	Yang Wang, Wenjie Zhang, Lin Wu, Xuemin Lin, Meng Fang, Shirui Pan	In this paper, 1) we propose a multi-graph laplacian regularized LRR with each graph laplacian corresponding to one view to characterize its local manifold structure.
301	Bayesian Optimization of Partition Layouts for Mondrian Processes	Yi Wang, Bin Li, Xuhui Fan, Yang Wang, Fang Chen	In this paper, we attempt to circumvent these drawbacks by proposing an alternative method for inferring the MP partition structure.
302	Bernoulli Random Forests: Closing the Gap between Theoretical Consistency and Empirical Soundness	Yisen Wang, Qingtao Tang, Shu-Tao Xia, Jia Wu, Xingquan Zhu	In this paper, we propose a Bernoulli random forests model (BRF), which intends to close the gap between the theoretical consistency and the empirical soundness of random forests classification.
303	Learning A Deep ℓ_∞ Encoder for Hashing	Zhangyang Wang, Yingzhen Yang, Shiyu Chang, Qing Ling, Thomas S. Huang	Based on the Alternating Direction Method of Multipliers (ADMM), we formulate the original convex minimization problem as a feed-forward neural network, named Deep L-infinity Encoder, by introducing the novel Bounded Linear Unit (BLU) neuron and modeling the Lagrange multipliers as network biases.
304	To Project More or to Quantize More: Minimize Reconstruction Bias for Learning Compact Binary Codes	Zhe Wang, Ling-Yu Duan, Junsong Yuan, Tiejun Huang, Wen Gao	We present a novel approach called Minimal Reconstruction Bias Hashing (MRH) to learn similarity preserving binary codes that jointly optimize both projection and quantization stages.
305	Deep Nonlinear Feature Coding for Unsupervised Domain Adaptation	Pengfei Wei, Yiping Ke, Chi Keong Goh	In this paper, we propose a Deep Nonlinear Feature Coding framework (DNFC) for unsupervised domain adaptation.
306	Discriminatively Trained Recurrent Neural Networks for Continuous Dimensional Emotion Recognition from Audio	Felix Weninger, Fabien Ringeval, Erik Marchi, Björn Schuller	Hence, in this paper we introduce a technique for the discriminative training of deep neural networks using the concordance correlation coefficient as cost function, which unites both correlation and mean squared error in a single differentiable function.
307	Preference Inference through Rescaling Preference Learning	Nic Wilson, Mojtaba Montazery	In this paper, we analyse which vectors are rescale-optimal, and when there is a unique rescale-optimal vector, and we consider how to compute the induced preference relation.
308	Budgeted Multi-Armed Bandits with Multiple Plays	Yingce Xia, Tao Qin, Weidong Ma, Nenghai Yu, Tie-Yan Liu	We study the multi-play budgeted multi-armed bandit (MP-BMAB) problem, in which pulling an arm receives both a random reward and a random cost, and a player pulls L( ≥ 1) arms at each round.
309	Multi-View Exclusive Unsupervised Dimension Reduction for Video-Based Facial Expression Recognition	Liping Xie, Dacheng Tao, Haikun Wei	Multi-View Exclusive Unsupervised Dimension Reduction for Video-Based Facial Expression Recognition
310	Robust and Sparse Fuzzy K-Means Clustering	Jinglin Xu, Junwei Han, Kai Xiong, Feiping Nie	In this paper, we present a robust and sparse fuzzy K-Means clustering algorithm, an extension to the standard fuzzy K-Means algorithm by incorporating a robust function, rather than the square data fitting term, to handle outliers.
311	Weight Features for Predicting Future Model Performance of Deep Neural Networks	Yasunori Yamada, Tetsuro Morimura	In this study, we propose using weight features extracted from network weights at an early stage of the learning process as explanation variables for predicting the eventual model performance.
312	i, Poet: Automatic Poetry Composition through Recurrent Neural Networks with Iterative Polishing Schema	Rui Yan	Hence, we propose a new generative model with a polishing schema, and output a refined poem composition.
313	Unsupervised Human Action Categorization with Consensus Information Bottleneck Method	Xiaoqiang Yan, Yangdong Ye, Xueying Qiu	To solve this problem, we present a novel and effective Consensus Information Bottleneck (CIB) method for unsupervised human action categorization.
314	Modularity Based Community Detection with Deep Learning	Liang Yang, Xiaochun Cao, Dongxiao He, Chuan Wang, Xiao Wang, Weixiong Zhang	Inspired by the strong representation power of deep neural networks, we propose a novel nonlinear reconstruction method by adopting deep neural networks for representation.
315	Sparsity Conditional Energy Label Distribution Learning for Age Estimation	Xu Yang, Xin Geng, Deyu Zhou	In this paper, Sparsity Conditional Energy Label Distribution Learning (SCE-LDL) is proposed to solve this problem.
316	Empirical Risk Minimization for Metric Learning Using Privileged Information	Xun Yang, Meng Wang, Luming Zhang, Dacheng Tao	To address this issue, in this paper we propose an effective metric learning method by exploiting privileged information to relax the fixed threshold under the empirical risk minimization framework.
317	A Unified Framework for Discrete Spectral Clustering	Yang Yang, Fumin Shen, Zi Huang, Heng Tao Shen	In this work, we study how to achieve discrete clustering as well as reliably generalize to unseen data.
318	Learning by Actively Querying Strong Modal Features	Yang Yang, De-Chuan Zhan, Yuan Jiang	In this paper, we propose a training strategy, ACQUEST (ACtive QUErying STrong modalities), which exploits strong modal information by actively querying the strong modal feature values of "selected" instances rather than their corresponding ground truths.
319	Multi-Modal Bayesian Embeddings for Learning Social Knowledge Graphs	Zhilin Yang, Jie Tang, William Cohen	The problem is referred to as learning social knowledge graphs.We propose a multi-modal Bayesian embedding model, GenVector, to learn latent topics that generate word and network embeddings.GenVector leverages large-scale unlabeled data with embeddings and represents data of two modalities — i.e., social network users and knowledge concepts — in a shared latent topic space.Experiments on three datasets show that the proposed method clearly outperforms state-of-the-art methods.
320	Greedy Learning of Generalized Low-Rank Models	Quanming Yao, James T. Kwok	In this paper, we develop a more flexible greedy algorithm for generalized low-rank models whose optimization objective can be smooth or nonsmooth, general convex or strongly convex.
321	Frequent Direction Algorithms for Approximate Matrix Multiplication with Applications in CCA	Qiaomin Ye, Luo Luo, Zhihua Zhang	In this paper, we propose a deterministic algorithm FD-AMM for computing an approximation to the product of two given matrices.
322	Neural Enquirer: Learning to Query Tables in Natural Language	Pengcheng Yin, Zhengdong Lu, Hang Li, Ben Kao	We propose Neural Enquirer — a neural network architecture for answering natural language (NL) questions based on a knowledge base (KB) table.
323	Theoretically-Grounded Policy Advice from Multiple Teachers in Reinforcement Learning Settings with Applications to Negative Transfer	Yusen Zhan, Haitham Bou Ammar, Matthew E. Taylor	This paper formally defines a setting where multiple teacher agents can provide advice to a student and introduces an algorithm to leverage both autonomous exploration and teacher’s advice.
324	Unsupervised Feature Learning from Time Series	Qin Zhang, Jia Wu, Hong Yang, Yingjie Tian, Chengqi Zhang	In this paper we study the problem of learning discriminative features (segments), often referred to as shapelets [Ye and Keogh, 2009] of time series, from unlabeled time series data.
325	Collaborative Filtering with Generalized Laplacian Constraint via Overlapping Decomposition	Qing Zhang, Houfeng Wang	To solve this problem, we propose a unified MF framework with generalized Laplacian constraint for collaborative filtering.
326	Large Scale Sparse Clustering	Ruqi Zhang, Zhiwu Lu	To address this challenging problem, we thus propose a large-scale sparse clustering (LSSC) algorithm.
327	Improving DCNN Performance with Sparse Category-Selective Objective Function	Shizhou Zhang, Yihong Gong, Jinjun Wang	In this paper, we choose to learn useful cues from object recognition mechanisms of the human visual cortex, and propose a DCNN performance improvement method without the need for increasing the network complexity.
328	Staleness-Aware Async-SGD for Distributed Deep Learning	Wei Zhang, Suyog Gupta, Xiangru Lian, Ji Liu	In this paper, we propose a variant of the ASGD algorithm in which the learning rate is modulated according to the gradient staleness and provide theoretical guarantees for convergence of this algorithm.
329	Self-Adapted Multi-Task Clustering	Xianchao Zhang, Xiaotong Zhang, Han Liu	Self-Adapted Multi-Task Clustering
330	Bayesian Dictionary Learning with Gaussian Processes and Sigmoid Belief Networks	Yizhe Zhang, Ricardo Henao, Chunyuan Li, Lawrence Carin	We propose a Bayesian framework for dictionary learning, with spatial location dependencies captured by imposing a multiplicative Gaussian process (GP) priors on the latent units representing binary activations.
331	Denoising and Completion of 3D Data via Multidimensional Dictionary Learning	Zemin Zhang, Shuchin Aeron	In this paper a new dictionary learning algorithm for multidimensional data is proposed.
332	Improving Top-N Recommendation with Heterogeneous Loss	Feipeng Zhao, Yuhong Guo	In this paper, we propose a novel personalized top-N recommendation approach that minimizes a combined heterogeneous loss based on linear self-recovery models.
333	Predictive Collaborative Filtering with Side Information	Feipeng Zhao, Min Xiao, Yuhong Guo	In this paper, we propose a novel predictive collaborative filtering approach that exploits both the partially observed user-item recommendation matrix and the item-based side information to produce top-N recommender systems.
334	Incomplete Multi-Modal Visual Data Grouping	Handong Zhao, Hongfu Liu, Yun Fu	Motivated by this question, we propose an unsupervised method which well handles the incomplete multi-modal data by transforming the original and incomplete data to a new and complete representation in a latent space.
335	Learning Cross-View Binary Identities for Fast Person Re-Identification	Feng Zheng, Ling Shao	In this paper, we propose to learn cross-view binary identities (CBI) for fast person re-identification.
336	Fast-and-Light Stochastic ADMM	Shuai Zheng, James T. Kwok	In this paper, we propose an integration of ADMM with the method of stochastic variance reduced gradient (SVRG).
337	Transfer Hashing with Privileged Information	Joey Tianyi Zhou, Xinxing Xu, Sinno Jialin Pan, Ivor W. Tsang, Zheng Qin, Rick Siow Mong Goh	To address this data sparsity issue in hashing, inspired by transfer learning, we propose a new framework named Transfer Hashing with Privileged Information (THPI).
338	Model-Based Deep Hand Pose Estimation	Xingyi Zhou, Qingfu Wan, Wei Zhang, Xiangyang Xue, Yichen Wei	In this work, we propose a model based deep learning approach that adopts a forward kinematics based layer to ensure the geometric validity of estimated poses.
339	Probabilistic Rank-One Matrix Analysis with Concurrent Regularization	Yang Zhou, Haiping Lu	To address these problems, this paper proposes a bilinear PPCA method named as Probabilistic Rank-One Matrix Analysis (PROMA).
340	Crowdsourcing via Tensor Augmentation and Completion	Yao Zhou, Jingrui He	Different from most existing work on crowdsourcing, which ignore the structure information in the labeling data provided by non-experts, in this paper, we propose a novel structured approach based on tensor augmentation and completion.
341	A Self-Representation Induced Classifier	Pengfei Zhu, Lei Zhang, Wangmeng Zuo, Xiangchu Feng, Qinghua Hu	We investigate the representation based classification problem from a rather different perspective in this paper, that is, we learn how each feature (i.e., each element) of a sample can be represented by the features of itself.
342	Stochastic Multiresolution Persistent Homology Kernel	Xiaojin Zhu, Ara Vartanian, Manish Bansal, Duy Nguyen, Luke Brandl	We introduce a new topological feature representation for point cloud objects.
343	Asymptotic Optimality of Myopic Optimization in Trial-Offer Markets with Social Influence	Andrés Abeliuk, Gerardo Berbeglia, Felipe Maldonado, Pascal Van Hentenryck	We study dynamic trial-offer markets, in which participants first try a product and later decide whether to purchase it or not.
344	Managing Overstaying Electric Vehicles in Park-and-Charge Facilities	Arpita Biswas, Ragavendran Gopalakrishnan, Partha Dutta	To analyze this central tradeoff, we develop a novel framework that integrates models for realistic user behavior into queueing dynamics to locate the optimal penalty from the points of view of utilization and revenue, for different values of the external charging demand.
345	A SAT-Based Approach for Mining Association Rules	Abdelhamid Boudane, Said Jabbour, Lakhdar Sais, Yakoub Salhi	In this paper, we propose a new propositional satisfiability based approach to mine association rules in a single step.
346	Taking Up the Gaokao Challenge: An Information Retrieval Approach	Gong Cheng, Weixi Zhu, Ziwei Wang, Jianghui Chen, Yuzhong Qu	As a preliminary attempt to take up this challenge, we focus on multiple-choice questions in Gaokao, and propose a three-stage approach that exploits and extends information retrieval techniques.
347	A Framework for Integrating Symbolic and Sub-Symbolic Representations	Keith Clark, Bernhard Hengst, Maurice Pagnucco, David Rajaratnam, Peter Robinson, Claude Sammut, Michael Thielscher	This paper establishes a framework that hierarchically integrates symbolic and sub-symbolic representations in an architecture for cognitive robotics.
348	Informed Expectations to Guide GDA Agents in Partially Observable Environments	Dustin Dannenhauer, Hector Munoz-Avila, Michael T. Cox	We present a formalism of the problem that includes sensing costs, a GDA algorithm using this formalism, an examination of four methods of expectations under this formalism, and an implementation of the algorithm and empirical study.
349	A Collaborative Filtering Approach to Citywide Human Mobility Completion from Sparse Call Records	Zipei Fan, Ayumi Arai, Xuan Song, Apichon Witayangkurn, Hiroshi Kanasugi, Ryosuke Shibasaki	In this paper, we model the completion problem as a recommender system and therefore solve this problem in a collaborative filtering (CF) framework.
350	Optimal Interdiction of Illegal Network Flow	Qingyu Guo, Bo An, Yair Zick, Chunyan Miao	Experimental evaluation shows that our approach can obtain a robust enough solution outperforming the existing methods and heuristic baselines significantly and scale up to realistic-sized problems.
351	Modifying MCTS for Human-Like General Video Game Playing	Ahmed Khalifa, Aaron Isaksen, Julian Togelius, Andy Nealen	We address the problem of making general video game playing agents play in a human-like manner.
352	Baseline Regularization for Computational Drug Repositioning with Longitudinal Observational Data	Zhaobin Kuang, James Thomson, Michael Caldwell, Peggy Peissig, Ron Stewart, David Page	To address the high-dimensional, irregular, subject and time-heterogeneous nature of EHRs, we propose Baseline Regularization (BR) and a variant that extend the one-way fixed effect model, which is a standard approach to analyze small-scale longitudinal data.
353	Predicting Confusion in Information Visualization from Eye Tracking and Interaction Data	Sébastien Lallé, Cristina Conati, Giuseppe Carenini	In this paper, we focus on predicting occurrences of confusion during the interaction with a visualization using eye tracking and mouse data.
354	Word Clouds with Latent Variable Analysis for Visual Comparison of Documents	Tuan M. V. Le, Hady W. Lauw	We therefore motivate the principle of displaying related words in a coherent manner, and propose to realize it through modeling the latent aspects of words.
355	Bayesian Nonparametric Collaborative Topic Poisson Factorization for Electronic Health Records-Based Phenotyping	Wonsung Lee, Youngmin Lee, Heeyoung Kim, Il-Chul Moon	To improve EHR-based phenotyping by bridging the separated methods, we present Bayesian nonparametric collaborative topic Poisson factorization (BN-CTPF) that is the first nonparametric content-based Poisson factorization and first application of jointly analyzing the phenotye topics and estimating the individual risk scores.
356	Household Structure Analysis via Hawkes Processes for Enhancing Energy Disaggregation	Liangda Li, Hongyuan Zha	To address this problem, we propose to model the influence between householders’ energy usage behaviors directly through a novel probabilistic model, which combines topic models with the Hawkes processes.
357	Protein Secondary Structure Prediction Using Cascaded Convolutional and Recurrent Neural Networks	Zhen Li, Yizhou Yu	Inspired by the recent successes of deep neural networks, in this paper, we propose an end-to-end deep network that predicts protein secondary structures from integrated local and global contextual features.
358	Makeup Like a Superstar: Deep Localized Makeup Transfer Network	Si Liu, Xinyu Ou, Ruihe Qian, Wei Wang, Xiaochun Cao	In this paper, we propose a novel Deep Localized Makeup Transfer Network to automatically recommend the most suitable makeup for a female and synthesis the makeup on her face.
359	Urban Water Quality Prediction Based on Multi-Task Multi-View Learning	Ye Liu, Yu Zheng, Yuxuan Liang, Shuming Liu, David S. Rosenblum	In this work, we forecast the water quality of a station over the next few hours, using a multi-task multi-view learning method to fuse multiple datasets from different domains.
360	Clustering Financial Time Series: How Long Is Enough?	Gautier Marti, Sébastien Andler, Frank Nielsen, Philippe Donnat	This paper sets up a statistical framework to study the validity of such practices.
361	Player Goal Recognition in Open-World Digital Games with Long Short-Term Memory Networks	Wookhee Min, Bradford Mott, Jonathan Rowe, Barry Liu, James Lester	In this paper, we formulate player goal recognition as a sequence labeling task and introduce a goal recognition framework based on long short-term memory (LSTM) networks.
362	How to Build Your Network? A Structural Analysis	Anastasia Moskvina, Jiamou Liu	In this paper, we study the process, which we call network building, of establishing ties between two existing social networks in order to reach certain structural goals.
363	Simulating Human Inferences in the Light of New Information: A Formal Analysis	Marco Ragni, Christian Eichhorn, Gabriele Kern-Isberner	In this article we consider the so-called Suppression Task, a core example in cognitive science about human reasoning that demonstrates that some additional information can lead to the suppression of simple inferences like the modus ponens.
364	Measuring Performance of Peer Prediction Mechanisms Using Replicator Dynamics	Victor Shnayder, Rafael M. Frongillo, David C. Parkes	Rather than assume agents counter-speculate and compute an equilibrium, we adopt replicator dynamics as a model for population learning.
365	DeepTransport: Prediction and Simulation of Human Mobility and Transportation Mode at a Citywide Level	Xuan Song, Hiroshi Kanasugi, Ryosuke Shibasaki	In this study, we collect big and heterogeneous data (e.g., GPS records and transportation network data), and we build an intelligent system, namely DeepTransport, for simulating and predicting human mobility and transportation mode at a citywide level.
366	Balancing Appearance and Context in Sketch Interpretation	Yale Song, Randall Davis, Kaichen Ma, Dana L. Penney	We describe a sketch interpretation system that detects and classifies clock numerals created by subjects taking the Clock Drawing Test, a clinical tool widely used to screen for cognitive impairments (e.g., dementia).
367	Semantic Question-Answering with Video and Eye-Tracking Data: AI Foundations for Human Visual Perception Driven Cognitive Film Studies	Jakob Suchan, Mehul Bhatt	We present a computational framework for the grounding and semantic interpretation of dynamic visuo-spatial imagery consisting of video and eye-tracking data.
368	Rating-Boosted Latent Topics: Understanding Users and Items with Ratings and Reviews	Yunzhi Tan, Min Zhang, Yiqun Liu, Shaoping Ma	In this paper, we exploit textual review information, as well as ratings, to model user preferences and item features in a shared topic space and subsequently introduce them into a matrix factorization model for recommendation.
369	Scene Text Detection in Video by Learning Locally and Globally	Shu Tian, Wei-Yi Pei, Ze-Yu Zuo, Xu-Cheng Yin	In this paper, we propose a unified tracking based text detection system by learning locally and globally, which uniformly integrates detection, tracking, recognition and their interactions.
370	Stochastic and-or Grammars: A Unified Framework and Logic Perspective	Kewei Tu	In this paper we propose a representation framework of stochastic AOGs that is agnostic to the type of the data being modeled and thus unifies various domain-specific AOGs.
371	Dimensionally Guided Synthesis of Mathematical Word Problems	Ke Wang, Zhendong Su	The goal of this work is to efficiently synthesize MWPs that are authentic (i.e., similar to manually written problems), diverse (i.e., covering a wide range of mathematical tasks), and configurable (i.e., varying difficulty levels and solution characteristics).
372	Scale-Adaptive Low-Resolution Person Re-Identification via Learning a Discriminating Surface	Zheng Wang, Ruimin Hu, Yi Yu, Junjun Jiang, Chao Liang, Jinqiao Wang	On this basis, we propose to learn a discriminating surface separating these feasible and infeasible functions in the scale-distance function space, and use it for reidentifying persons.
373	On Modeling and Predicting Individual Paper Citation Count over Time	Shuai Xiao, Junchi Yan, Changsheng Li, Bo Jin, Xiangfeng Wang, Xiaokang Yang, Stephen M. Chu, Hongyuan Zha	We propose a method for predicting the citation counts of individual publications, over an arbitrary time period.
374	Exploiting Problem Structure in Combinatorial Landscapes: A Case Study on Pure Mathematics Application	Xiao-Feng Xie, Zun-Jing Wang	In this paper, we present a method using AI techniques to solve a case of pure mathematics applications for finding narrow admissible tuples.
375	Modeling Contagious Merger and Acquisition via Point Processes with a Profile Regression Prior	Junchi Yan, Shuai Xiao, Changsheng Li, Bo Jin, Xiangfeng Wang, Bin Ke, Xiaokang Yang, Hongyuan Zha	We propose to use a mutually-exciting point process with a regression prior to quantify the investor’s M&A behavior.
376	POISketch: Semantic Place Labeling over User Activity Streams	Dingqi Yang, Bin Li, Philippe Cudré-Mauroux	In this paper, we tackle the problem of semantic place labeling over user activity streams.
377	ST-MVL: Filling Missing Values in Geo-Sensory Time Series Data	Xiuwen Yi, Yu Zheng, Junbo Zhang, Tianrui Li	In this paper, we propose a spatio-temporal multi-view-based learning (ST-MVL) method to collectively fill missing readings in a collection of geo-sensory time series data, considering 1) the temporal correlation between readings at different timestamps in the same series and 2) the spatial correlation between different time series.
378	Nonlinear Hierarchical Part-Based Regression for Unconstrained Face Alignment	Xiang Yu, Zhe Lin, Shaoting Zhang, Dimitris N. Metaxas	In this paper, we propose a robust two-stage hierarchical regression approach, to solve a popular Human-Computer Interaction, the unconstrained face-in-the-wild keypoint detection problem for computers.
379	Situation Testing-Based Discrimination Discovery: A Causal Inference Approach	Lu Zhang, Yongkai Wu, Xintao Wu	In this paper, we develop a general technique to capture discrimination based on the legally grounded situation testing methodology.
380	Maximum Sustainable Yield Problem for Robot Foraging and Construction System	Ruohan Zhang, Zhao Song	We propose an adaptive algorithm to overcome the problem that resource growth model is often unknown. We introduce the Maximum Sustainable Yield problem for a multi-robot foraging and construction system, inspired by the relationship between the natural resource growth and harvesting behaviors in an ecosystem.
381	Personalizing EEG-Based Affective Models with Transfer Learning	Wei-Long Zheng, Bao-Liang Lu	In this paper, we propose to build personalized EEG-based affective models without labeled target data using transfer learning techniques.
382	WikiWrite: Generating Wikipedia Articles Automatically	Siddhartha Banerjee, Prasenjit Mitra	In this work, we propose WikiWrite, a system capable of generating content for new Wikipedia articles automatically.
383	A Discriminative Approach to Grounded Spoken Language Understanding in Interactive Robotics	Emanuele Bastianelli, Danilo Croce, Andrea Vanzo, Roberto Basili, Daniele Nardi	In this work, a standard linguistic pipeline for semantic parsing is extended toward a form of perceptually informed natural language processing that combines discriminative learning and distributional semantics.
384	Distraction-Based Neural Networks for Modeling Document	Qian Chen, Xiaodan Zhu, Zhenhua Ling, Si Wei, Hui Jiang	In this paper, we propose neural models to train computers not just to pay attention to specific regions and content of input documents with attention models, but also distract them to traverse between different content of a document so as to better grasp the overall meaning for summarization.
385	Agreement-Based Joint Training for Bidirectional Attention-Based Neural Machine Translation	Yong Cheng, Shiqi Shen, Zhongjun He, Wei He, Hua Wu, Maosong Sun, Yang Liu	We propose agreement-based joint training for bidirectional attention-based end-to-end neural machine translation.
386	Neural Network Translation Models for Grammatical Error Correction	Shamil Chollampatt, Kaveh Taghipour, Hwee Tou Ng	In this paper, we address these limitations by using two different yet complementary neural network models, namely a neural network global lexicon model and a neural network joint model.
387	Recognizing Opinion Sources Based on a New Categorization of Opinion Types	Lingjia Deng, Janyce Wiebe	Different from previous works which categorize an opinion according to whether the source is the writer or the source is a noun phrase, we propose a new categorization of opinions according to the role that the source plays.
388	Hashtag Recommendation Using Attention-Based Convolutional Neural Network	Yuyun Gong, Qi Zhang	Motivated by the successful use of convolutional neural networks (CNNs) for many natural language processing tasks, in this paper, we adopt CNNs to perform the hashtag recommendation problem.
389	Intersubjectivity and Sentiment: From Language to Knowledge	Lin Gui, Ruifeng Xu, Yulan He, Qin Lu, Zhongyu Wei	In this work, we make use of intersubjectivity as the basis to model shared stance and subjectivity for sentiment analysis.
390	Parse Tree Fragmentation of Ungrammatical Sentences	Homa B. Hashemi, Rebecca Hwa	We introduce a framework for reviewing the parses of ungrammatical sentences and extracting the coherent parts whose syntactic analyses make sense.
391	Exploiting N-Best Hypotheses to Improve an SMT Approach to Grammatical Error Correction	Duc Tam Hoang, Shamil Chollampatt, Hwee Tou Ng	In this paper, we propose a novel approach to improve the accuracy of GEC, by exploiting the n-best hypotheses generated by an SMT approach.
392	Generating Recommendation Evidence Using Translation Model	Jizhou Huang, Shiqi Zhao, Shiqiang Ding, Haiyang Wu, Mingming Sun, Haifeng Wang	This paper proposes a statistical model consisting of four sub-models to generate evidences for entities, which can help users better understand each recommended entity, and figure out the connections between the recommended entities and a given query.
393	Tree-State Based Rule Selection Models for Hierarchical Phrase-Based Machine Translation	Shujian Huang, Huifeng Sun, Chengqi Zhao, Jinsong Su, Xin-Yu Dai, Jiajun Chen	In this paper, we propose tree-state models to discriminate the good or bad usage of translation rules based on the syntactic structures of the source sentence.
394	Bag-of-Embeddings for Text Classification	Peng Jin, Yue Zhang, Xingyuan Chen, Yunqing Xia	We study a conceptually simple classification model by exploiting multi-prototype word embeddings based on text classes.
395	Reinforcement Learning for Turn-Taking Management in Incremental Spoken Dialogue Systems	Hatim Khouzaimi, Romain Laroche, Fabrice Lefèvre	In this article, reinforcement learning is used to learn an optimal turn-taking strategy for vocal human-machine dialogue.
396	Joint Models for Extracting Adverse Drug Events from Biomedical Text	Fei Li, Yue Zhang, Meishan Zhang, Donghong Ji	In this paper, we investigate joint models for simultaneously extracting drugs, diseases and adverse drug events.
397	StalemateBreaker: A Proactive Content-Introducing Approach to Automatic Human-Computer Conversation	Xiang Li, Lili Mou, Rui Yan, Ming Zhang	In this paper, we propose STALEMATEBREAKER, a conversation system that can proactively introduce new content when appropriate.
398	Towards Zero Unknown Word in Neural Machine Translation	Xiaoqing Li, Jiajun Zhang, Chengqing Zong	To tackle this problem, we propose a novel substitution-translation-restoration method.
399	Learning Paraphrase Identification with Structural Alignment	Chen Liang, Praveen Paritosh, Vinodh Rajendran, Kenneth D. Forbus	Here, we propose a new alignment-based approach to learn semantic similarity.
400	Knowledge Representation Learning with Entities, Attributes and Relations	Yankai Lin, Zhiyuan Liu, Maosong Sun	In this paper, we distinguish existing KG-relations into attributes and relations, and propose a new KR model with entities, attributes and relations (KR-EAR).
401	Recurrent Neural Network for Text Classification with Multi-Task Learning	Pengfei Liu, Xipeng Qiu, Xuanjing Huang	In this paper, we use the multi-task learning framework to jointly learn across multiple related tasks.
402	Exploring Segment Representations for Neural Segmentation Models	Yijia Liu, Wanxiang Che, Jiang Guo, Bing Qin, Ting Liu	In this paper, we combine semi-CRF with neural network to solve NLP segmentation tasks.
403	HC-Search for Incremental Parsing	Yijia Liu, Wanxiang Che, Bing Qin, Ting Liu	We learn our incremental parsing model with a relaxed learning objective.
404	Automatic Construction and Evaluation of a Large Semantically Enriched Wikipedia	Alessandro Raganato, Claudio Delli Bovi, Roberto Navigli	In this paper we present the automatic construction and evaluation of a Semantically Enriched Wikipedia in which the overall number of linked mentions has been more than tripled solely by exploiting the structure of Wikipedia itself and the wide-coverage sense inventory of BabelNet. As a result we obtain a sense-annotated corpus with more than 200 million annotations of over 4 million different concepts and named entities.
405	Cross-Lingual Dataless Classification for Many Languages	Yangqiu Song, Shyam Upadhyay, Haoruo Peng, Dan Roth	We use CLESA (cross-lingual explicit semantic analysis) to embed both foreign language documents and an English label space into a shared semantic space, and select the best label(s) for a document using the similarity between the corresponding semantic representations.
406	Robust Natural Language Processing — Combining Reasoning, Cognitive Semantics, and Construction Grammar for Spatial Language	Michael Spranger, Jakob Suchan, Mehul Bhatt	We present a system for generating and understanding of dynamic and static spatial relations in robotic interaction setups.
407	Sparse Word Embeddings Using ℓ₁ Regularized Online Learning	Fei Sun, Jiafeng Guo, Yanyan Lan, Jun Xu, Xueqi Cheng	Inspired by the success of sparse models in enhancing interpretability, we propose to introduce sparse constraint into Word2Vec.
408	Match-SRNN: Modeling the Recursive Matching Structure with Spatial RNN	Shengxian Wan, Yanyan Lan, Jun Xu, Jiafeng Guo, Liang Pang, Xueqi Cheng	Based on this idea, we propose a novel deep architecture, namely Match-SRNN, to model the recursive matching structure.
409	Employing External Rich Knowledge for Machine Comprehension	Bingning Wang, Shangmin Guo, Kang Liu, Shizhu He, Jun Zhao	In this paper, we build an attention-based recurrent neural network model, train it with the help of external knowledge which is semantically relevant to machine comprehension, and achieves a new state-of-art result.
410	Building Joint Spaces for Relation Extraction	Chang Wang, Liangliang Cao, James Fan	In this paper, we present a novel approach for relation extraction using only term pairs as the input without textual features.
411	Chinese Song Iambics Generation with Neural Attention-Based Model	Qixin Wang, Tianyi Luo, Dong Wang, Chao Xing	This paper applies the attention-based sequence-to-sequence model to generate Chinese Song iambics.
412	A Bilingual Graph-Based Semantic Model for Statistical Machine Translation	Rui Wang, Hai Zhao, Sabine Ploux, Bao-Liang Lu, Masao Utiyama	This paper presents Bilingual Graph-based Semantic Model (BGSM) to alleviate such shortcomings.
413	Diverse Image Captioning via GroupTalk	Zhuhao Wang, Fei Wu, Weiming Lu, Jun Xiao, Xi Li, Zitong Zhang, Yueting Zhuang	In this paper, we propose a framework called GroupTalk to learn multiple image caption distributions simultaneously and effectively mimic the diversity of the image captions written by human beings.
414	Representation Learning of Knowledge Graphs with Hierarchical Types	Ruobing Xie, Zhiyuan Liu, Maosong Sun	In this paper, we propose a novel method named Type-embodied Knowledge Representation Learning (TKRL) to take advantages of hierarchical entity types.
415	Neural Generative Question Answering	Jun Yin, Xin Jiang, Zhengdong Lu, Lifeng Shang, Hang Li, Xiaoming Li	This paper presents an end-to-end neural network model, named Neural Generative Question Answering (GENQA), that can generate answers to simple factoid questions, based on the facts in a knowledge-base.
416	Unsupervised Word and Dependency Path Embeddings for Aspect Term Extraction	Yichun Yin, Furu Wei, Li Dong, Kaimeng Xu, Ming Zhang, Ming Zhou	In this paper, we develop a novel approach to aspect term extraction based on unsupervised learning of distributed representations of words and dependency paths.
417	Collaborative Multi-Level Embedding Learning from Reviews for Rating Prediction	Wei Zhang, Quan Yuan, Jiawei Han, Jianyong Wang	In this paper, we propose a novel Collaborative Multi-Level Embedding (CMLE) model to address these limitations.
418	A Joint Model of Intent Determination and Slot Filling for Spoken Language Understanding	Xiaodong Zhang, Houfeng Wang	Based on the idea that the intent and semantic slots of a sentence are correlative, we propose a joint model for both tasks.
419	Expert Finding for Community-Based Question Answering via Ranking Metric Network Learning	Zhou Zhao, Qifan Yang, Deng Cai, Xiaofei He, Yueting Zhuang	In this paper, we consider the problem of expert finding from the viewpoint of learning ranking metric embedding.
420	Context-Specific and Multi-Prototype Character Representations	Xiaoqing Zheng, Jiangtao Feng, Mengxiao Lin, Wenqiang Zhang	We present a neural network architecture to jointly learn character embeddings and induce context representations from large data sets.
421	Unsupervised Storyline Extraction from News Articles	Deyu Zhou, Haiyang Xu, Xin-Yu Dai, Yulan He	In this paper, we propose a non-parametric generative model to extract structured representations and evolution patterns of storylines simultaneously.
422	Hierarchical Planning: Relating Task and Goal Decomposition with Task Sharing	Ron Alford, Vikas Shivashankar, Mark Roberts, Jeremy Frank, David W. Aha	Another example is the Action Notation Markup Language (ANML) which adds aspects of generative planning and task-sharing to the standard HTN semantics.The aim of this work is to formally analyze the effects of these modifications to HTN semantics on the computational complexity and expressivity of HTN planning.
423	Markovian State and Action Abstractions for MDPs via Hierarchical MCTS	Aijun Bai, Siddharth Srivastava, Stuart Russell	In this context we propose a hierarchical Monte Carlo tree search algorithm and show that it converges to a recursively optimal hierarchical policy.
424	Which Contingent Events to Observe for the Dynamic Controllability of a Plan	Arthur Bit-Monnot, Malik Ghallab, Félix Ingrand	We propose a first procedure for testing their dynamic controllability.
425	Factored Probabilistic Belief Tracking	Blai Bonet, Hector Geffner	Factored Probabilistic Belief Tracking
426	Maintaining Evolving Domain Models	Dan Bryce, J. Benton, Michael W. Boldt	In this work, we address this model maintenance problem in a system called Marshal.
427	A Branch-and-Price Algorithm for Scheduling Observations on a Telescope	Nicolas Catusse, Hadrien Cambazard, Nadia Brauner, Pierre Lemaire, Bernard Penz, Anne-Marie Lagrange, Pascal Rubini	We prove that the problem is NP-complete, and we propose a constraint- programming-based branch-and-price algorithm to solve it.
428	Improved Solvers for Bounded-Suboptimal Multi-Agent Path Finding	Liron Cohen, Tansel Uras, T. K. Satish Kumar, Hong Xu, Nora Ayanian, Sven Koenig	In this first feasibility study, we develop a bounded-suboptimal MAPF solver, Improved-ECBS or iECBS(w1) for short, that has sub optimality factor w1 rather than w1w2 (because it uses experience graphs to guide its search without inflating the heuristic values) and can run faster than ECBS(w1).
429	Online Symbolic Gradient-Based Optimization for Factored Action MDPs	Hao Cui, Roni Khardon	This paper investigates online stochastic planning for problems with large factored state and action spaces.
430	∃-STRIPS: Existential Quantification in Planning and Constraint Satisfaction	Guillem Francès, Hector Geffner	In this work, we argue that existential variables are an essential feature for representing and reasoning with constraints in planning, and that it is harmful to compile them away or avoid them altogether, since this hides part of the problem structure that can be exploited computationally.
431	Learning to Rank for Synthesizing Planning Heuristics	Caelan Reed Garrett, Leslie Pack Kaelbling, Tomás Lozano-Pérez	Thus, we instead frame learning a heuristic as a learning to rank problem which we solve using a RankSVM formulation.
432	Robust Repositioning to Counter Unpredictable Demand in Bike Sharing Systems	Supriyo Ghosh, Michael Trick, Pradeep Varakantham	We propose an online and robust repositioning approach to minimise the loss in customer demand while considering the possible uncertainty in future demand.
433	Generalizing the Edge-Finder Rule for the Cumulative Constraint	Vincent Gingras, Claude-Guy Quimper	We present two novel filtering algorithms for the Cumulative constraint based on a new energetic relaxation.
434	Decoupled Strong Stubborn Sets	Daniel Gnad, Martin Wehrle, Jörg Hoffmann	Recent work has introduced fork-decoupled search, addressing classical planning problems where a single center component provides preconditions for several leaf components.
435	Demand Prediction and Placement Optimization for Electric Vehicle Charging Stations	Ragavendran Gopalakrishnan, Arpita Biswas, Alefiya Lightwala, Skanda Vasudevan, Partha Dutta, Abhishek Tripathi	This work addresses these concerns, making the following three novel contributions: (i) a supervised multi-view learning framework using Canonical Correlation Analysis (CCA) for demand prediction at candidate sites, using multiple datasets such as points of interest information, traffic density, and the historical usage at existing charging stations; (ii) a mixed-packing-and-covering optimization framework that models competing concerns of the service provider and EV users; (iii) an iterative heuristic to solve these problems by alternately invoking knapsack and setcover algorithms.
436	A POMDP Approach to Influence Diagram Evaluation	Eric A. Hansen, Jinchuan Shi, Arindam Khaled	We propose a node-removal/arc-reversal algorithm for influence diagram evaluation that includes reductions that allow an influence diagram to be solved by a generalization of the dynamic programming approach to solving partially observable Markov decision processes (POMDPs).
437	Anticipatory Troubleshooting	Netantel Hasidi, Roni Stern, Meir Kalech, Shulamit Reches	We propose an anticipatory troubleshooting algorithm, which is able to reason about both current and future failures.
438	Hierarchical Model Predictive Control for Multi-Robot Navigation	Chao Huang, Xin Chen, Yifan Zhang, Shengchao Qin, Yifeng Zeng, Xuandong Li	In this paper, we propose a Hierarchical Model Predictive Control scheme that employs reachable sets to decouple the navigation problem of linear dynamical multi-robot systems.
439	Batch-Switching Policy Iteration	Shivaram Kalyanakrishnan, Utkarsh Mall, Ritish Goyal	We introduce Batch-Switching Policy Iteration (BSPI), a family of deterministic PI algorithms that switches states in "batches," taking the batch size b as a parameter.
440	In Search of Tractability for Partial Satisfaction Planning	Michael Katz, Vitaly Mirkis	In this work we investigate the computational complexity of restricted fragments of two variants of partial satisfaction: net-benefit and oversubscription planning.
441	State-Dependent Cost Partitionings for Cartesian Abstractions in Classical Planning	Thomas Keller, Florian Pommerening, Jendrik Seipp, Florian Geißer, Robert Mattmüller	We introduce state-dependent cost partitionings which take context information of actions into account, and show that an optimal state-dependent cost partitioning dominates its state-independent counterpart.
442	Privacy Preserving Plans in Partially Observable Environments	Sarah Keren, Avigdor Gal, Erez Karpas	In this work we present a framework that supports the offline analysis of goal recognition settings with non-deterministic system sensor models, in which the observer has partial (and possibly noisy) observability of the agent’s actions, while the agent is assumed to have full observability of his environment.
443	Sequential Planning for Steering Immune System Adaptation	Christian Kroer, Tuomas Sandholm	In this paper we study steering such adaptation through sequential planning.
444	Heuristic Subset Selection in Classical Planning	Levi H. S. Lelis, Santiago Franco, Marvin Abisrror, Mike Barley, Sandra Zilles, Robert C. Holte	In this paper we present greedy methods for selecting a subset of heuristic functions for guiding A* search.
445	Learning Predictive State Representations via Monte-Carlo Tree Search	Yunlong Liu, Hexing Zhu, Yifeng Zeng, Zongxiong Dai	In this paper, with the benefits of Monte-Carlo tree search (MCTS) for finding solutions in complex problems, and by proposing the concept of model entropy for measuring the model accuracy as the evaluation function in MCTS, the discovery problem is formalized as a sequential decision making problem.
446	Automatic Generation of High-Level State Features for Generalized Planning	Damir Lotinac, Javier Segovia-Aguas, Sergio Jiménez, Anders Jonsson	This paper presents a novel method to automatically generate high-level state features for solving a generalized planning problem.
447	Planning for a Single Agent in a Multi-Agent Environment Using FOND	Christian Muise, Paolo Felli, Tim Miller, Adrian R. Pearce, Liz Sonenberg	In this paper, we cast the problem of planning in a multi-agent environment as one of Fully-Observable Non-Deterministic (FOND) planning.
448	Heuristic Planning for PDDL+ Domains	Wiktor Piotrowski, Maria Fox, Derek Long, Daniele Magazzeni, Fabio Mercorio	In this paper we introduce DiNo, a new planner capable of tackling complex problems with non-linear system dynamcs governing the continuous evolution of states.
449	PAC Greedy Maximization with Efficient Bounds on Information Gain for Sensor Selection	Yash Satsangi, Shimon Whiteson, Frans A. Oliehoek	We present probably approximately correct greedy maximization, which requires access only to cheap anytime confidence bounds on F and uses them to prune elements.
450	Heuristics for Numeric Planning via Subgoaling	Enrico Scala, Patrik Haslum, Sylvie Thiébaux	The paper presents a new relaxation for hybrid planning with continuous numeric and propositional state variables based on subgoaling, generalising the subgoaling principle underlying the hmax and hadd heuristics to such problems.
451	Hierarchical Finite State Controllers for Generalized Planning	Javier Segovia-Aguas, Sergio Jiménez, Anders Jonsson	In this paper we introduce the concept of hierarchical FSCs for planning by allowing controllers to call other controllers.
452	Correlation Complexity of Classical Planning Domains	Jendrik Seipp, Florian Pommerening, Gabriele Röger, Malte Helmert	We analyze how complex a heuristic function must be to directly guide a state-space search algorithm towards the goal.
453	Blind Search for Atari-Like Online Planning Revisited	Alexander Shleyfman, Alexander Tuisov, Carmel Domshlak	We show that the effectiveness of the blind state-space search for Atari-like online planning can be pushed even further by focusing the search using both structural state similarity and the relative myopic value of the states.
454	Plan Recognition as Planning Revisited	Shirin Sohrabi, Anton V. Riabov, Octavian Udrea	In this paper, we propose to extend previous work to (1) address observations over fluents, (2) better address unreliable observations (i.e., noisy or missing observations), and (3) recognize plans in addition to goals.
455	On State-Dominance Criteria in Fork-Decoupled Search	álvaro Torralba, Daniel Gnad, Patrick Dubbert, Jörg Hoffmann	We devise several more powerful criteria, show that they preserve optimality, and establish their interrelations.
456	Abstraction Heuristics for Symbolic Bidirectional Search	álvaro Torralba, Carlos Linares López, Daniel Borrajo	We propose a novel way to use abstraction heuristics in symbolic bidirectional search in which the search only resorts to heuristics when it becomes unfeasible.
457	Goal Recognition Design with Stochastic Agent Action Outcomes	Christabel Wayllace, Ping Hou, William Yeoh, Tran Cao Son	In this paper, we generalize the GRD problem to Stochastic GRD (S-GRD) problems, which handle stochastic action outcomes.
458	Graph-Based Factorization of Classical Planning Problems	Martin Wehrle, Silvan Sievers, Malte Helmert	In this paper, we propose a generic approach for factorizing a classical planning problem into an equivalent problem with fewer operator and variable dependencies.
459	Structural Symmetries for Fully Observable Nondeterministic Planning	Dominik Winterer, Martin Wehrle, Michael Katz	We generalize the concepts of structural symmetries and symmetry reduction to FOND planning and specifically to the LAO* algorithm.
460	Resolving Over-Constrained Conditional Temporal Problems Using Semantically Similar Alternatives	Peng Yu, Jiaying Shen, Peter Z. Yeh, Brian Williams	In this paper, we present an approach for solving such over-constrained problems, by also relaxing non-temporal variable domains through the consideration of additional options that are semantically similar.
461	Co-Optimizating Multi-Agent Placement with Task Assignment and Scheduling	Chongjie Zhang, Julie A. Shah	To enable large-scale multi-agent coordination under temporal and spatial constraints, we formulate it as a multi-level optimization problem and develop a multi-abstraction search approach for co-optimizing agent placement with task assignment and scheduling.
462	Commitment Semantics for Sequential Decision Making under Reward Uncertainty	Qi Zhang, Edmund Durfee, Satinder Singh, Anna Chen, Stefan Witwicki	We consider the additional complication in cases where an agent might prefer to change its policy as it learns more about its reward function from experience.
463	Action Recognition with Joints-Pooled 3D Deep Convolutional Descriptors	Congqi Cao, Yifan Zhang, Chunjie Zhang, Hanqing Lu	In this paper, we present a simple, yet effective method to aggregate convolutional activations of a 3D deep convolutional neural network (3D CNN) into discriminative descriptors based on joint positions.
464	3D Action Recognition Using Multi-Temporal Depth Motion Maps and Fisher Vector	Chen Chen, Mengyuan Liu, Baochang Zhang, Jungong Han, Junjun Jiang, Hong Liu	This paper presents an effective local spatio-temporal descriptor for action recognition from depth video sequences.
465	Clustering-Based Joint Feature Selection for Semantic Attribute Prediction	Lin Chen, Baoxin Li	In this paper, we propose a novel feature selection approach which embeds attribute correlation modeling in multi-attribute joint feature selection.
466	Semi-Supervised Multimodal Deep Learning for RGB-D Object Recognition	Yanhua Cheng, Xin Zhao, Rui Cai, Zhiwei Li, Kaiqi Huang, Yong Rui	To address this problem, we propose a semi-supervised multimodal deep learning framework to train DCNN effectively based on very limited labeled data and massive unlabeled data.
467	Precision Instrument Targeting via Image Registration for the Mars 2020 Rover	Gary Doran, David R. Thompson, Tara Estlin	We employ existing ORB features for landmark-based image registration, describe and theoretically justify a novel approach to filtering false landmark matches, and employ a random forest classifier to automatically reject failed alignments.
468	Discriminative Log-Euclidean Feature Learning for Sparse Representation-Based Recognition of Faces from Videos	Mohammed E. Fathy, Azadeh Alavi, Rama Chellappa	In this paper, we represent image sets as dictionaries of Symmetric Positive Definite (SPD) matrices that are more robust to local deformations and outliers.
469	Highly Accurate Gaze Estimation Using a Consumer RGB-D Sensor	Reza Shoja Ghiass, Ognjen Arandjelovic	In this paper we describe a highly accurate algorithm that performs gaze estimation using an affordable and widely available device such as Kinect.
470	Making Robots Proactive through Equilibrium Maintenance	Jasmin Grosinger, Federico Pecora, Alessandro Saffiotti	This paper introduces the notion of equilibrium maintenance as a contribution to this understanding.
471	Robust Iterative Quantization for Efficient ℓ_p-norm Similarity Search	Yuchen Guo, Guiguang Ding, Jungong Han, Xiaoming Jin	In this paper, we propose an ITQ+ algorithm, aiming to enhance both robustness and generalization of the original ITQ algorithm.
472	Semantic Analysis for Crowded Scenes Based on Non-Parametric Tracklet Clustering	Allam S. Hassanein, Mohamed E. Hussein, Walid Gomaa	In this paper we address the problem of semantic analysis of structured / unstructured crowded video scenes.
473	Online Multi-Object Tracking by Quadratic Pseudo-Boolean Optimization	Long Lan, Dacheng Tao, Chen Gong, Naiyang Guan, Zhigang Luo	Here we introduce quadratic pseudo-Boolean optimization (QPBO) to an online MOT model to analyze frequent interactions.
474	Robust Joint Discriminative Feature Learning for Visual Tracking	Xiangyuan Lan, Shengping Zhang, Pong C. Yuen	To address this problem, different from other feature fusion-based trackers which consider one of these two aspects only, this paper proposes an unified feature learning framework which simultaneously exploits both the representation capability and the discriminability of multiple features for visual tracking.
475	Saliency Transfer: An Example-Based Method for Salient Object Detection	Xin Li, Fan Yang, Leiting Chen, Hongbin Cai	In this paper, we propose a novel method for salient object detection that involves the transfer of the annotations from an existing example onto an input image.
476	What Is Where: Inferring Containment Relations from Videos	Wei Liang, Yibiao Zhao, Yixin Zhu, Song-Chun Zhu	In this paper, we present a probabilistic approach to explicitly infer containment relations between objects in 3D scenes.
477	A Stochastic Image Grammar for Fine-Grained 3D Scene Reconstruction	Xiaobai Liu, Yadong Mu, Liang Lin	This paper presents a stochastic grammar for fine-grained 3D scene reconstruction from a single image.
478	Unsupervised Learning on Neural Network Outputs: With Application in Zero-Shot Learning	Yao Lu	For an application, we proposed a new zero-shot learning method, in which the visual features learned by PCA/ICA are employed.
479	Geometric Scene Parsing with Hierarchical LSTM	Zhanglin Peng, Ruimao Zhang, Xiaodan Liang, Xiaobai Liu, Liang Lin	To achieve these goals, we propose a novel recurrent neural network model, named Hierarchical Long Short-Term Memory (H-LSTM).
480	Incorporating Prototype Theory in Convolutional Neural Networks	Babak Saleh, Ahmed Elgammal, Jacob Feldman	We propose computational models to improve the generalization capacity of CNNs by considering how typical a training image looks like.
481	Learning Social Affordance for Human-Robot Interaction	Tianmin Shu, M. S. Ryoo, Song-Chun Zhu	In this paper, we present an approach for robot learning of social affordance from human activity videos.
482	Learning to Order Objects Using Haptic and Proprioceptive Exploratory Behaviors	Jivko Sinapov, Priyanka Khante, Maxwell Svetlik, Peter Stone	This paper proposes a novel framework that enables a robot to learn ordinal object relations.
483	Crowd Scene Understanding with Coherent Recurrent Neural Networks	Hang Su, Yinpeng Dong, Jun Zhu, Haibin Ling, Bo Zhang	To address these issues, we present a Coherent Long Short Term Memory (cLSTM) network to capture the nonlinear crowd dynamics by learning an informative representation of crowd motions, which facilitates the critical tasks in crowd scene analysis.
484	Learning Multi-Modal Grounded Linguistic Semantics by Playing “I Spy”	Jesse Thomason, Jivko Sinapov, Maxwell Svetlik, Peter Stone, Raymond J. Mooney	In this paper, we build perceptual models that use haptic, auditory, and proprioceptive data acquired through robot exploratory behaviors to go beyond vision.
485	Beyond Object Recognition: Visual Sentiment Analysis with Deep Coupled Adjective and Noun Neural Networks	Jingwen Wang, Jianlong Fu, Yong Xu, Tao Mei	In this paper, we propose a novel visual sentiment analysis approach with deep coupled adjective and noun neural networks.
486	Visual Tracking with Reliable Memories	Shu Wang, Shaoting Zhang, Wei Liu, Dimitris N. Metaxas	In this paper, we propose a novel visual tracking framework that intelligently discovers reliable patterns from a wide range of video to resist drift error for long-term tracking tasks.
487	Object Recognition with Hidden Attributes	Xiaoyang Wang, Qiang Ji	Unlike the existing approaches that treat attributes as a middle level representation and require to estimate the attributes during testing, we propose to incorporate the hidden attributes, which are the attributes used only during training to improve model learning and are not needed during testing.
488	Rule-Based Programming of Molecular Robot Swarms for Biomedical Applications	Inbal Wiesel-Kapah, Gal A. Kaminka, Guy Hachmon, Noa Agmon, Ido Bachelet	We present a novel approach to generating such swarms from a treatment program.
489	Object-Based World Modeling in Semi-Static Environments with Dependent Dirichlet Process Mixtures	Lawson L. S. Wong, Thanard Kurutach, Tomás Lozano-Pérez, Leslie Pack Kaelbling	We derive a novel MAP inference algorithm under this model, subject to data association constraints.
490	Enforcing Template Representability and Temporal Consistency for Adaptive Sparse Tracking	Xue Yang, Fei Han, Hua Wang, Hao Zhang	In this paper, we propose a novel sparse tracking algorithm that well addresses temporal appearance changes, by enforcing template representability and temporal consistency (TRAC).
491	Synthesizing Robotic Handwriting Motion by Learning from Human Demonstrations	Hang Yin, Patrícia Alves-Oliveira, Francisco S. Melo, Aude Billard, Ana Paiva	This paper contributes a novel framework that enables a robotic agent to efficiently learn and synthesize believable handwriting motion.
492	Bridging Saliency Detection to Weakly Supervised Object Detection Based on Self-Paced Curriculum Learning	Dingwen Zhang, Deyu Meng, Long Zhao, Junwei Han	To this end, this paper first comprehensively analyzes the challenges in applying saliency detection to WOD.
493	Semantics-Aware Deep Correspondence Structure Learning for Robust Person Re-Identification	Yaqing Zhang, Xi Li, Liming Zhao, Zhongfei Zhang	In this paper, we propose an end-to-end deep correspondence structure learning (DCSL) approach to address the cross-camera person-matching problem in the person re-identification task.
494	Video-Based Person Re-Identification by Simultaneously Learning Intra-Video and Inter-Video Distance Metrics	Xiaoke Zhu, Xiao-Yuan Jing, Fei Wu, Hui Feng	In this paper, we propose a simultaneous intra-video and inter-video distance learning (SI2DL) approach for video-based person re-id.
495	Contextual Symmetries in Probabilistic Graphical Models	Ankit Anand, Aditya Grover, Mausam, Parag Singla	An important approach for efficient inference in probabilistic graphical models exploits symmetries among objects in the domain.
496	Algorithmic Improvements in Approximate Counting for Probabilistic Inference: From Linear to Logarithmic SAT Calls	Supratik Chakraborty, Kuldeep S. Meel, Moshe Y. Vardi	We present a new approach to hashing-based approximate model counting in which the number of oracle invocations grows logarithmically in n, while still providing strong theoretical guarantees.
497	Incorporating Knowledge into Structural Equation Models Using Auxiliary Variables	Bryant Chen, Judea Pearl, Elias Bareinboim	In this paper, we extend graph-based identification methods by allowing background knowledge in the form of non-zero parameter values.
498	Solving M-Modes Using Heuristic Search	Cong Chen, Changhe Yuan, Chao Chen	This paper introduces new algorithms that directly search the space of consistent local modes in finding the global modes, which is enabled by a novel search operator designed to search a subgraph of variables at each time.
499	Probabilistic Inference Modulo Theories	Rodrigo de Salvo Braz, Ciaran O’Reilly, Vibhav Gogate, Rina Dechter	We present SGDPLL(T), an algorithm that solves (among many other problems) probabilistic inference modulo theories, that is, inference problems over probabilistic models defined via a logic theory provided as a parameter (currently, propositional, equalities on discrete sorts, and inequalities, more specifically difference arithmetic, on bounded integers).
500	Adaptive Budget Allocation for Maximizing Influence of Advertisements	Daisuke Hatano, Takuro Fukunaga, Ken-ichi Kawarabayashi	Our main contribution in this paper is to analyze adaptive strategies for the budget allocation problem.
501	A Symbolic Closed-Form Solution to Sequential Market Making with Inventory	Shamin Kinathil, Scott Sanner, Sanmay Das, Nicolás Della Penna	In this work we introduce a novel continuous state POMDP framework which is the first to solve, exactly and in closed-form, the optimal market making problem with inventory, fixed asset value, arbitrary belief state priors, trader models and reward functions via symbolic dynamic programming.
502	Approximate Probabilistic Inference with Bounded Error for Hybrid Probabilistic Logic Programming	Steffen Michels, Arjen Hommersom, Peter J.F. Lucas	In this paper, we propose a hybrid extension for probabilistic logic programming, which allows for exact inference for a much wider class of continuous distributions than existing extensions.
503	Group Decision Making via Probabilistic Belief Merging	Nico Potyka, Erman Acar, Matthias Thimm, Heiner Stuckenschmidt	We propose a probabilistic-logical framework for group decision-making.
504	Probably Approximately Correct Learning in Stochastic Games with Temporal Logic Specifications	Min Wen, Ufuk Topcu	Building on the idea of the R-MAX algorithm, we propose a probably approximately correct (PAC) learning algorithm that can learn such a strategy efficiently in an online manner with a-priori unknown reward functions and unknown transition distributions.
505	Swift: Compiled Inference for Probabilistic Programming Languages	Yi Wu, Lei Li, Stuart Russell, Rastislav Bodik	This paper describes Swift, a compiler for the BLOG PPL.
506	Latent Contextual Bandits and their Application to Personalized Recommendations for New Users	Li Zhou, Emma Brunskill	In this paper we propose Latent Contextual Bandits.
507	A Generative Model for Recognizing Mixed Group Activities in Still Images	Zheng Zhou, Kan Li, Xiangjian He, Mengmeng Li	In this paper, we propose a generative model to provide a more reasonable interpretation for the mixed group activities contained in one image.
508	ATUCAPTS: Automated Tests that a User Cannot Pass Twice Simultaneously	Garrett Andersen, Vincent Conitzer	In this paper, we propose ATUCAPTS (Automated Tests That a User Cannot Pass Twice Simultaneously) as a solution.
509	Domain Adaptation for Learning from Label Proportions Using Self-Training	Ehsan Mohammady Ardehaly, Aron Culotta	To do so, we propose a domain adaptation algorithm that uses an LLP model fit on the source domain to generate label proportions for the target domain.
510	Inferring Motif-Based Diffusion Models for Social Networks	Qing Bao, William K. Cheung, Jiming Liu	In this paper, we postulate that the latent temporal activation patterns (or motifs) of nodes of different social roles form the underlying information diffusion mechanisms generating the information cascades observed over a social network.
511	Non-Objection Inference for Inconsistency-Tolerant Query Answering	Salem Benferhat, Zied Bouraoui, Madalina Croitoru, Odile Papini, Karim Tabia	We propose a novel non-objection inference relation (along with its variants) where a query is considered as valid if it follows from at least one repair and it is consistent with all the repairs.
512	Multi-Source Iterative Adaptation for Cross-Domain Classification	Himanshu S. Bhatt, Arun Rajkumar, Shourya Roy	In this work, we present a novel multi-source iterative domain adaptation algorithm (MSIDA) that leverages knowledge from selective sources to improve the performance in a target domain.
513	Timeline Summarization from Social Media with Life Cycle Models	Yi Chang, Jiliang Tang, Dawei Yin, Makoto Yamada, Yan Liu	In this paper, we investigate the problem of timeline summarization and propose a novel framework Timeline-Sumy, which consists of episode detecting and summary ranking.
514	HIEDS: A Generic and Efficient Approach to Hierarchical Dataset Summarization	Gong Cheng, Cheng Jin, Yuzhong Qu	We present an efficient solution, to serve users with dynamically configured summaries with acceptable latency.
515	Assessing Translation Ability through Vocabulary Ability Assessment	Yo Ehara, Yukino Baba, Masao Utiyama, Eiichiro Sumita	In this paper, we propose a practical method for assessing translation ability.
516	Weakly-Supervised Deep Learning for Customer Review Sentiment Classification	Ziyu Guan, Long Chen, Wei Zhao, Yi Zheng, Shulong Tan, Deng Cai	In this work, we focus on customer reviews which are an important form of opinionated content.
517	Questimator: Generating Knowledge Assessments for Arbitrary Topics	Qi Guo, Chinmay Kulkarni, Aniket Kittur, Jeffrey P. Bigham, Emma Brunskill	This paper introduces Questimator, an automated system that generates multiple-choice assessment questions for any topic contained within Wikipedia.
518	Efficient Algorithms for Spanning Tree Centrality	Takanori Hayashi, Takuya Akiba, Yuichi Yoshida	In this paper, we propose efficient algorithms for estimating spanning tree centralities with theoretical guarantees on their accuracy.
519	Sherlock: Sparse Hierarchical Embeddings for Visually-Aware One-Class Collaborative Filtering	Ruining He, Chunbin Lin, Jianguo Wang, Julian McAuley	Here, we address these challenges by building such structures to model the visual dimensions across different product categories.
520	Ordering Concepts Based on Common Attribute Intensity	Tatsuya Iwanari, Naoki Yoshinaga, Nobuhiro Kaji, Toshiharu Nishina, Masashi Toyoda, Masaru Kitsuregawa	This paper presents a novel task of ordering given concepts (e.g., London, Paris, and Rome) on the basis of common attribute intensity expressed by a given adjective (e.g., safe) and proposes statistical ordering methods that integrate heterogeneous evidence extracted from text on concept ordering.
521	Real-Time Web Scale Event Summarization Using Sequential Decision Making	Chris Kedzie, Fernando Diaz, Kathleen McKeown	We present a system based on sequential decision making for the online summarization of massive document streams, such as those found on the web.
522	Identifying Key Observers to Find Popular Information in Advance	Takuya Konishi, Tomoharu Iwata, Kohei Hayashi, Ken-ichi Kawarabayashi	To identify efficient observers, we propose a feature selection based framework.
523	Matching via Dimensionality Reduction for Estimation of Treatment Effects in Digital Marketing Campaigns	Sheng Li, Nikos Vlassis, Jaya Kawale, Yun Fu	To address this problem, we propose a novel estimator that first projects the data to a number of random linear subspaces, and it then estimates the median treatment effect by nearest-neighbor matching in each subspace.
524	What Does Social Media Say about Your Stress?	Huijie Lin, Jia Jia, Liqiang Nie, Guangyao Shen, Tat-Seng Chua	To address these problems, we devise a comprehensive scheme to measure a user’s stress level from his/her social media data. In particular, we first build a benchmark dataset and extract a rich set of stress-oriented features.
525	Learning to Incentivize: Eliciting Effort via Output Agreement	Yang Liu, Yiling Chen	In this paper, we focus on using output agreement mechanisms to elicit effort, in addition to eliciting truthful answers, from a population of workers.
526	Pay Me and I’ll Follow You: Detection of Crowdturfing Following Activities in Microblog Environment	Yuli Liu, Yiqun Liu, Min Zhang, Shaoping Ma	In this work, we try to solve the voluntary following problem through a newly proposed detection method named DetectVC.
527	Item Recommendation for Emerging Online Businesses	Chun-Ta Lu, Sihong Xie, Weixiang Shao, Lifang He, Philip S. Yu	In this paper, we introduce a novel similarity measure, AmpSim (Augmented Meta Path-based Similarity) that takes both the linkage structures and the augmented link attributes into account.
528	Collaborative Evolution for User Profiling in Recommender Systems	Zhongqi Lu, Sinno Jialin Pan, Yong Li, Jie Jiang, Qiang Yang	In this work, we come up with a novel evolutionary view of user’s profile by proposing a Collaborative Evolution (CE) model, which learns the evolution of user’s profiles through the sparse historical data in recommender systems and outputs the prospective user profile of the future.
529	Browsing Regularities in Hedonic Content Systems	Ping Luo, Ganbin Zhou, Jiaxi Tang, Rui Chen, Zhongjie Yu, Qing He	We found that despite differences in visit time and user types, the distribution over browsing length for a visit can be described by the inverse Gaussian form with a very high precision.
530	Detecting Rumors from Microblogs with Recurrent Neural Networks	Jing Ma, Wei Gao, Prasenjit Mitra, Sejeong Kwon, Bernard J. Jansen, Kam-Fai Wong, Meeyoung Cha	This paper presents a novel method that learns continuous representations of microblog events for identifying rumors.
531	Dynamic Task Allocation Algorithm for Hiring Workers that Learn	Shengying Pan, Kate Larson, Josh Bradshaw, Edith Law	In this work, we explore the question of how to hire workers who can learn over time.
532	Learning Deep Intrinsic Video Representation by Exploring Temporal Coherence and Graph Structure	Yingwei Pan, Yehao Li, Ting Yao, Tao Mei, Houqiang Li, Yong Rui	In this paper, we present a novel approach to learn the deep video representation by exploring both local and holistic contexts.
533	WebGazer: Scalable Webcam Eye Tracking Using User Interactions	Alexandra Papoutsaki, Patsorn Sangkloy, James Laskey, Nediyana Daskalova, Jeff Huang, James Hays	We introduce WebGazer, an online eye tracker that uses common webcams already present in laptops and mobile devices to infer the eye-gaze locations of web visitors on a page in real time.
534	Cross-Media Shared Representation by Hierarchical Learning with Multiple Deep Networks	Yuxin Peng, Xin Huang, Jinwei Qi	For addressing the above problems, we propose the cross-media multiple deep network (CMDN) to exploit the complex cross-media correlation by hierarchical learning.
535	Practical Linear Models for Large-Scale One-Class Collaborative Filtering	Suvash Sedhain, Hung Bui, Jaya Kawale, Nikos Vlassis, Branislav Kveton, Aditya Krishna Menon, Trung Bui, Scott Sanner	We show that it is possible to scale up linear recommenders to big data by learning an OC-CF model in a randomized low-dimensional embedding of the user-item interaction matrix.
536	Intervention Strategies for Increasing Engagement in Crowdsourcing: Platform, Predictions, and Experiments	Avi Segal, Ya’akov (Kobi) Gal, Ece Kamar, Eric Horvitz, Alex Bowyer, Grant Miller	We evaluate this approach on Galaxy Zoo, one of the largest citizen science application on the web, where we traced the behavior and contributions of thousands of users who received intervention messages over a period of a few months.
537	A Framework for Recommending Relevant and Diverse Items	Chaofeng Sha, Xiaowei Wu, Junyu Niu	In this paper, we propose a general framework to recommend relevant and diverse items which explicitly takes the coverage of user interest into account.
538	Understanding Information Diffusion under Interactions	Yuan Su, Xi Zhang, Philip S. Yu, Wen Hua, Xiaofang Zhou, Binxing Fang	In this paper, we investigate the contagion adoption behavior by incorporating various types of interactions into a coherent model, and propose a novel interaction-aware diffusion framework called IAD.
539	Progressive Comparison for Ranking Estimation	Ryusuke Takahama, Toshihiro Kamishima, Hisashi Kashima	In this paper, we propose an efficient data collection method called progressive comparison, whose objective is to collect many pairwise comparison data while reducing the number of evaluations.
540	Max-Margin DeepWalk: Discriminative Learning of Network Representation	Cunchao Tu, Weicheng Zhang, Zhiyuan Liu, Maosong Sun	In this paper, we overcome this challenge by proposing a novel semi-supervised model, max-margin DeepWalk (MMDW).
541	KOGNAC: Efficient Encoding of Large Knowledge Graphs	Jacopo Urbani, Sourav Dutta, Sairam Gurajada, Gerhard Weikum	We propose KOGNAC, a dictionary-encoding algorithm designed to improve SPARQL querying with a judicious combination of statistical and semantic techniques.
542	Learning Hostname Preference to Enhance Search Relevance	Jingjing Wang, Changsung Kang, Yi Chang, Jiawei Han	In this study, we learn the hostname preference of queries, which are further utilized to enhance search relevance.
543	Bayesian Probabilistic Multi-Topic Matrix Factorization for Rating Prediction	Keqiang Wang, Wayne Xin Zhao, Hongwei Peng, Xiaoling Wang	To develop a more principled solution for LMF, this paper presents a Bayesian Probabilistic Multi-Topic Matrix Factorization model.
544	Causality Based Propagation History Ranking in Social Networks	Zheng Wang, Chaokun Wang, Jisheng Pei, Xiaojun Ye, Philip S. Yu	To reduce this information overload, in this paper, we present the problem of propagation history ranking.
545	Learning Defining Features for Categories	Bo Xu, Chenhao Xie, Yi Zhang, Yanghua Xiao, Haixun Wang, Wei Wang	We formalize the defining feature learning problem and propose a bootstrapping solution to learn defining features from the features of entities belonging to a category.
546	Deep Semantic-Preserving and Ranking-Based Hashing for Image Retrieval	Ting Yao, Fuchen Long, Tao Mei, Yong Rui	Observing that semantic structures carry complementary information, we propose the idea of co-training for hashing, by jointly learning projections from image representations to hash codes and classification.
547	Modeling the Homophily Effect between Links and Communities for Overlapping Community Detection	Hongyi Zhang, Tong Zhao, Irwin King, Michael R. Lyu	In this paper, we propose a Homophily-based Nonnegative Matrix Factorization (HNMF) to model both-sided relationships between links and communities.
548	Matrix Factorization+ for Movie Recommendation	Lili Zhao, Zhongqi Lu, Sinno Jialin Pan, Qiang Yang	We present a novel model for movie recommendations using additional visual features extracted from pictorial data like posters and still frames, to better understand movies.
549	Content-Driven Detection of Cyberbullying on the Instagram Social Network	Haoti Zhong, Hao Li, Anna Squicciarini, Sarah Rajtmajer, Christopher Griffin, David Miller, Cornelia Caragea	We study detection of cyberbullying in photo-sharing networks, with an eye on developing early warning mechanisms for the prediction of posted images vulnerable to attacks.
550	Learning Compact Visual Representation with Canonical Views for Robust Mobile Landmark Search	Lei Zhu, Jialie Shen, Xiaobai Liu, Liang Xie, Liqiang Nie	This paper proposes a Canonical View based Compact Visual Representation (2CVR) to handle these problems via novel three stage learning.
551	Rational-Based Visual Planning Monitors	Zohreh Alavi	My goal is to develop a framework for fully integrated planning, execution and vision in dynamic environments.
552	Online Fair Division Redux	Martin Aleksandrov	Can we improve the food allocation?
553	Lifting Techniques for Sequential Decision Making and Probabilistic Inference (Extended Abstract)	Ankit Anand	In probabilistic inference, we expand the notion of unconditional symmetries to contextual symmetries and apply them in Markov Chain Monte Carlo (MCMC) methods.
554	Combining Logic and Probability: P-log Perspective	Evgenii Balai	In particular, I aim to extend P-log with new constructs, clarify its semantics, develop a new efficient inference engine for it and establish its relationship with other related formalisms.
555	Planning under Uncertainty and Temporally Extended Goals	Alberto Camacho	The nature of these problems suggests that planning approaches could be used to find solutions efficiently.
556	Bounded Suboptimal Multi-Agent Path Finding Using Highways	Liron Cohen, Sven Koenig	Our objective is to minimize the the total arrival time.
557	Logic-Based Inductive Synthesis of Efficient Programs	Andrew Cropper	We describe an algorithm proven to learn minimal resource complexity robot strategies, and we propose future work to generalise the approach to a broader class of programs.
558	Transfer Learning for Multiagent Reinforcement Learning Systems	Felipe Leno da Silva, Anna Helena Reali Costa	This research aims to propose a Transfer Learning (TL) framework to accelerate learning by exploiting two knowledge sources: (i) previously learned tasks; and (ii) advising from a more experienced agent.
559	An Approach to Cooperation in General-Sum Normal Form Games	Steven Damer	An Approach to Cooperation in General-Sum Normal Form Games
560	Self Monitoring, Goal Driven Autonomy Agents	Dustin Dannenhauer	My current work aims to achieve agents that can reason about and use expectations in both dynamic and partially observable domains, as well as investigating meta-cognitive expectations for detecting anomalies in the agent’s own cognitive processes (reasoning, planning, etc) instead of anomalies in the world.
561	Fast Motion Prediction for Collaborative Robotics	Claudia Pérez-D’Arpino, Julie A. Shah	With this goal in mind, we focus on real-time target prediction of human reaching motion and present an algorithm based on time series classification.
562	Location-Based Activity Recognition with Hierarchical Dirichlet Process	Negar Ghourchian	In this work, we present a novel high-level strategy for mobility data analysis based on Hierarchical Dirichlet process, which is a powerful probabilistic model for clustering grouped data.
563	Action Selection Methods for Multi-Agent Navigation in Crowded Environments	Julio Godoy	In my thesis work, each agent has only a limited sensing range and uses online action selection techniques to dynamically adapt its motion to the local conditions.
564	Proactivity in Robots	Jasmin Grosinger	In my work I introduce the notion of equilibrium maintenance as a contribution to this understanding.
565	Probabilistic Planning with Risk-Sensitive Criterion	Ping Hou	With this motivation in mind, we revisit the Risk-Sensitive criterion (RS-criterion), where the objective is to find a policy that maximizes the probability that the cumulative cost is within some user-defined cost threshold.
566	Computer-Aided Game Design: Doctoral Consortium Research Abstract	Aaron Isaksen	Using models of human-like imperfect play, one can estimate how quantitative changes to a game will impact a player’s qualitative experience.
567	Stochastic Planning in Large Search Spaces	Bilal Kartal	For the patrolling problem, we present a novel stochastic search technique, Monte Carlo Tree Search with Useful Cycles, that can generate optimal cyclic patrol policies with theoretical convergence guarantees.
568	Extractive and Abstractive Event Summarization over Streaming Web Text	Chris Kedzie, Kathleen McKeown	In this thesis, we develop two extractive summarization systems for streaming text data.
569	Machine Learning for Integer Programming	Elias B. Khalil	Instead, I propose to use machine learning (ML) approaches such as supervised ranking and multi-armed bandits to make better-informed, input-specific decisions during MIP branch-and-bound.
570	Toward a Robust and Universal Crowd-Labeling Framework	Faiza Khan Khattak	We propose methods that estimate the labeler and data instance related parameters using frequentist and Bayesian approaches.
571	Active Inference for Dynamic Bayesian Networks	Caner Komurlu	In this research, we use dynamic Bayesian networks to model temporal systems and we apply active inference to dynamically choose variables for observation so as to improve prediction on unobserved variables
572	Learning Robust Representations for Data Analytics	Sheng Li	To address this challenge, we have proposed several effective methods to extract robust data representations, such as balanced graphs, discriminative subspaces, and robust dictionaries.
573	Modelling Satisfiability Problems: Theory and Practice	Valentin Mayer-Eichberger	Modelling Satisfiability Problems: Theory and Practice
574	Solving Hard Subgraph Problems in Parallel	Ciaran McCreesh	We investigate variable and value ordering heuristics, different inference strategies, intelligent backtracking search (backjumping), and bit- and thread-parallelism to exploit modern hardware.
575	Semantic Framework for Industrial Analytics and Diagnostics	Gulnar Mehdi, Sebastian Brandt, Mikhail Roshchin, Thomas Runkler	Our thesis investigates if semantic technology can be a potential solution to interact and leverage data analytics for operational use.
576	Adaptive Sequential Recommendation Using Context Trees	Fei Mi, Boi Faltings	We formalize the recommendation problem as a sequence prediction problem and compare different recommendation methods, including a new method called context tree (CT).
577	On the Synergy of Network Science and Artificial Intelligence	Decebal Constantin Mocanu	This Ph.D. research takes an alternative approach to the reductionism strategy, and tries to advance both fields, i.e. artificial intelligence and network science, by searching for the synergy between them, while not ignoring any other source of inspiration, e.g. neuroscience.
578	Are Spiking Neural Networks Useful for Classifying and Early Recognition of Spatio-Temporal Patterns?	Banafsheh Rekabdar	This paper presents a novel, unsupervised approach for learning, recognizing and early classifying spatio-temporal patterns using spiking neural networks for human robotic domains.
579	Reactive Policy Checking for Action Languages	Zeynep Gözen Saribatur	This thesis aims to build foundations for such a verification capability for policies with a reactive behavior, with a focus on combining the representation power of action languages with model checking techniques.
580	Towards Understanding Natural Language: Semantic Parsing, Commonsense Knowledge Acquisition and Applications	Arpit Sharma	In this work we present our progress towards these milestones of NLU.
581	General Statistical Approaches to Procedural Map Generation	Sam Snodgrass	We are interested in exploring statistical algorithms that could lead to generalized procedural map generators.
582	Time Decomposition for Diagnosis of Discrete Event Systems (Extended Abstract)	Xingyu Su	This work proposes Time-Window Algorithms (TWAs), which are extensions to IWAs.
583	Integrating Social Network Structure into Online Feature Selection	Antonela Tommasel	This thesis proposes an online feature selection technique for high-dimensional data based on both social and content-based information for the real-time classification of short-text streams coming from social media.
584	Conceptual Visualization and Navigation Methods for Polyadic Formal Concept Analysis	Diana Troanca	The goal of my thesis is to find visualization and navigation paradigms that can be applied to higher-dimensional datasets.
585	Automated Narrative Information Extraction Using Non-Linear Pipelines	Josep Valls-Vargas	We propose the use of domain knowledge to improve core NLP tasks and the overall performance of our system.
586	Reasoning about Space and Change with Answer Set Programming Modulo Theories	Przemysław Andrzej Wałęga	The aim of my work is to establish a computational framework for commonsense spatial reasoning about dynamic domains.
587	A Framework for Anomaly Reasoning: Interpretation through Concept Formation for Knowledge Transfer and Lifelong Learning	John Winder	As a first approach, I propose an interpretation method in which agents form concepts from perceptions to create new representations for use in planning and decision making.
588	BiPOCL: A Discourse-Driven Story Planner for Procedural Narrative Generation (Extended Abstract)	David R. Winer	This extended abstract describes an approach to narrative planning in which constraints for a story are discovered as part of the search for compatible story and discourse solutions.
589	Quantitative Path-Planning from Qualitative Language Instructions	Daqing Yi	We propose a framework supporting a robot that plans a path using information specified by a human using natural language; the path may contain multiple criteria and topological requirements.
590	Towards Intelligent Visual Understanding under Minimal Supervision	Dingwen Zhang	To alleviate this problem, we propose to develop novel visual understanding algorithms which can learn informative visual patterns under minimal (none or very weak) supervision and thus facilitate higher-level intelligence of the visual understanding systems.
591	On Ranking and Choice Models	Shivani Agarwal	This paper highlights recent developments in two areas of ranking and choice modeling that cross traditional boundaries and are of multidisciplinary interest: ranking from pairwise comparisons, and automatic discovery of latent categories from choice survey data.
592	Computational Social Choice: Some Current and New Directions	Haris Aziz	In this article, I discuss some current and new directions in the field.
593	Ontology-Mediated Query Answering: Harnessing Knowledge to Get More from Data	Meghyn Bienvenu	This paper briefly introduces OMQA and gives an overview of two recent lines of research.
594	Preference Restrictions in Computational Social Choice: Recent Progress	Edith Elkind, Martin Lackner, Dominik Peters	The goal of this short paper is to provide an overview of recent progress in understanding and exploiting useful properties of restricted preference domains, such as, e.g., the domains of single-peaked, single-crossing and 1-Euclidean preferences.
595	Boolean Satifiability and Beyond: Algorithms, Analysis, and AI Applications	Matti Järvisalo	Boolean Satifiability and Beyond: Algorithms, Analysis, and AI Applications
596	Directions in Hybrid Intelligence: Complementing AI Systems with Human Intelligence	Ece Kamar	This paper reviews recent research efforts towards developing hybrid systems focusing on reasoning methods for optimizing access to human intelligence and on gaining comprehensive understanding of humans as helpers of AI systems.
597	Open Information Extraction Systems and Downstream Applications	Mausam	We survey its use in both human-facing applications and downstream NLP tasks, including event schema induction, sentence similarity, text comprehension, learning word vector embeddings, and more.
598	Plausible Reasoning Based on Qualitative Entity Embeddings	Steven Schockaert, Shoaib Jameel	Subsequently, we advocate the use of qualitative abstractions of these vector spaces, as they are easier to obtain and manipulate, among others, while still supporting various forms of plausible reasoning.
599	A Hard Look at Soft Concepts	Dafna Shahaf	We present two approaches for tackling such challenges — an axiomatic one and a data-driven one — and demonstrate our ideas on two real-world applications: finding narratives in large textual corpora and identifying humorous cartoon captions.
600	First-Order Model Counting in a Nutshell	Guy Van den Broeck	We give an overview of model counting as it is applied in statistical relational learning, probabilistic programming, databases, and hybrid reasoning.
601	Sequential Decision Making for Improving Efficiency in Urban Environments	Pradeep Varakantham	Our broad approach to generating these sequential decision strategies is through a combination of data analytics (to obtain a model) and multi-stage optimization (planning/scheduling) under uncertainty (to solve the model).
602	Adversarial AI	Yevgeniy Vorobeychik	Adversarial AI
603	From Non-Convex Aggregates to Monotone Aggregates in ASP	Mario Alviano, Wolfgang Faber, Martin Gebser	In this paper, this gap is filled by means of a polynomial, faithful, and modular translation function, which can introduce disjunction in rule heads.
604	On the Properties of GZ-Aggregates in Answer Set Programming	Mario Alviano, Nicola Leone	A detailed complexity analysis of coherence testing and cautious reasoning under the new semantics highlighted similarities and differences versus mainstream stable model semantics for aggregates, which eventually led to the design of compilation techniques for implementing the new semantics on top of existing ASP solvers.
605	Optimal Prosumer Decision-Making Using Factored MDPs	Angelos Angelidakis, Georgios Chalkiadakis	In this work, we model, for the first time, this problem as a factored Markov Decision Process.
606	Hashing-Based Approximate Probabilistic Inference in Hybrid Domains: An Abridged Report	Vaishak Belle, Guy Van den Broeck, Andrea Passerini	In this work, we consider the problem of approximating inference tasks for a probability distribution defined over discrete and continuous random variables.
607	Optimal and Adaptive Algorithms for Online Boosting	Alina Beygelzimer, Satyen Kale, Haipeng Luo	We study online boosting, the task of converting any weak online learner into a strong online learner.
608	Detecting Student Emotions in Computer-Enabled Classrooms	Nigel Bosch, Sidney K. D’Mello, Ryan S. Baker, Jaclyn Ocumpaugh, Valerie Shute, Matthew Ventura, Lubin Wang, Weinan Zhao	We use computer vision, learning analytics, and machine learning to detect students’ affect in the real-world environment of a school computer lab that contained as many as thirty students at a time.
609	Learning Qualitative Spatial Relations for Robotic Navigation	Abdeslam Boularias, Felix Duvallet, Jean Oh, Anthony Stentz	In this work, we show how to ground nouns and navigation modes by learning from examples demonstrated by humans.
610	On Broken Triangles	Martin C. Cooper, Achref= El Mouelhi, Cyril Terrioux, Bruno Zanuttini	We show that a local version of the BTP allows the merging of domain values in binary CSPs, thus providing a novel polynomial-time reduction operation.
611	Sequencing Operator Counts	Toby O. Davies, Adrian R. Pearce, Peter J. Stuckey, Nir Lipovetzky	We exploit this information using a SAT-based approach which given an operator-count, either finds a valid plan; or generates a generalized landmark constraint violated by that count.
612	Tabling as a Library with Delimited Control	Benoit Desouter, Marko van Dooren, Tom Schrijvers, Alexander Vandenbroucke	To enable more widespread adoption, this paper presents a novel implementation of tabling for Prolog that is both high-level and compact.
613	Büchi, Lindenbaum, Tarski: A Program Analysis Appetizer	Vijay D’Silva, Caterina Urban	We describe a research programme to establish precise, mathematical correspondences between these approaches and to develop new analyzers using these results.
614	Effective Planning with More Expressive Languages	Guillem Francès, Hector Geffner	To address this, we show how relaxed plan heuristics can be lifted to a variable-free first-order planning language, Functional STRIPS, where atomic formulas can involve arbitrary terms.
615	Domain Model Acquisition in the Presence of Static Relations in the LOP System	Peter Gregory, Stephen Cresswell	We present a new domain model acquisition algorithm, LOP, that induces static predicates by using a combination of the generalised output from LOCM2 and a set of optimal plans as input to the learning system.
616	A Nearly-Linear Time Framework for Graph-Structured Sparsity	Chinmay Hegde, Piotr Indyk, Ludwig Schmidt	We introduce a framework for sparsity structures defined via graphs.
617	Observability, Identifiability and Sensitivity of Vision-Aided Inertial Navigation	Joshua Hernandez, Konstantine Tsotsos, Stefano Soatto	We analyze the observability of 3-D position and orientation from the fusion of visual and inertial sensors.
618	Projection, Inference, and Consistency	John N. Hooker	We show that inference in propositional logic can be achieved by Benders decomposition, an optimization method based on projection.
619	The Dependence of Effective Planning Horizon on Model Accuracy	Nan Jiang, Alex Kulesza, Satinder Singh, Richard Lewis	In this paper we provide a precise explanation for this phenomenon based on principles of learning theory.
620	Fleet Design Optimisation from Historical Data Using Constraint Programming and Large Neighbourhood Search	Philip Kilby, Tommaso Urli	We present an original approach to compute efficient mid-term fleet configurations at the request of a Queensland-based long-haul trucking carrier.
621	Deep Neural Decision Forests	Peter Kontschieder, Madalina Fiterau, Antonio Criminisi, Samuel Rota Bulò	We present a novel approach to enrich classification trees with the representation learning ability of deep (neural) networks within an end-to-end trainable architecture.
622	Proximal Gradient Temporal Difference Learning Algorithms	Bo Liu, Ji Liu, Mohammad Ghavamzadeh, Sridhar Mahadevan, Marek Petrik	In this paper, we describe proximal gradient temporal difference learning, which provides a principled way for designing and analyzing true stochastic gradient temporal difference learning algorithms.
623	Generating Tests for Robotized Painting Using Constraint Programming	Morten Mossige, Arnaud Gotlieb, Hein Meling	To address these challenges, we have developed and deployed a cost-effective, automated test generation technique aimed at validating the timing behavior of the process control system.
624	A Decision Procedure for (Co)datatypes in SMT Solvers	Andrew Reynolds, Jasmin Christian Blanchette	We describe a decision procedure to reason about such types.
625	Why Prices Need Algorithms	Tim Roughgarden, Inbal Talgam-Cohen	In this note we survey our main results from [Roughgarden and Talgam-Cohen, 2015], which show that the existence of equilibria in markets is inextricably connected to the computational complexity of related optimization problems, such as revenue or welfare maximization.
626	Online Bellman Residual and Temporal Difference Algorithms with Predictive Error Guarantees	Wen Sun, J. Andrew Bagnell	We establish connections from optimizing Bellman Residual and Temporal Difference Loss to worst-case long-term predictive error.
627	Welfare Effects of Market Making in Continuous Double Auctions: Extended Abstract	Elaine Wah, Mason Wright, Michael P. Wellman	We employ empirical simulation-based methods to evaluate heuristic strategies for market makers as well as background investors in a variety of complex trading environments.
628	Improving Topic Model Stability for Effective Document Exploration	Yi Yang, Shimei Pan, Yangqiu Song, Jie Lu, Mercan Topkara	In this study, we investigate the stability problem in topic modeling.
629	MATHCHECK: A Math Assistant via a Combination of Computer Algebra Systems and SAT Solvers	Edward Zulkoski, Vijay Ganesh, Krzysztof Czarnecki	We present a method and an associated system, called MathCheck, that embeds the functionality of a computer algebra system (CAS) within the inner loop of a conflict-driven clause-learning SAT solver.
630	Repairing General-Purpose ASR Output to Improve Accuracy of Spoken Sentences in Specific Domains Using Artificial Development Approach	C. Anantaram, Sunil Kumar Kopparapu, Chirag Patel, Aditya Mittal	We present an artificial development (Art-Dev) based mechanism for such a repair.
631	Practical 3D Tracking Using Low-Cost Cameras	Roman Barták, Michal Koutný, David Obdrzálek	This paper shows the possibility to track a single object using low-cost cameras on an ordinary laptop in a small-scale and mostly static environment.
632	Baby Tartanian8: Winning Agent from the 2016 Annual Computer Poker Competition	Noam Brown, Tuomas Sandholm	We demonstrate a winning agent from the 2016 Annual Computer Poker Competition, Baby Tartanian8.
633	KBQA: An Online Template Based Question Answering System over Freebase	Wanyun Cui, Yanghua Xiao, Wei Wang	KBQA: An Online Template Based Question Answering System over Freebase
634	SMACk: An Argumentation Framework for Opinion Mining	Mauro Dragoni, Célia da Costa Pereira, Andrea G.B. Tettamanzi, Serena Villata	Its growing relevance is mainly due to the impact of exploiting such techniques in different application domains from social science analysis to personal advertising.In this demo, we present our opinion summary application built on top of an argumentation framework, a standard AI framework whose value is to exchange, communicate and resolve possibly conflicting viewpoints in distributed scenarios.
635	A Virtual Assistant to Help Dysphagia Patients Eat Safely at Home	Michael Freed, Brian Burns, Aaron Heller, Daniel Sanchez, Sharon Beaumont-Bowman	We have developed an early prototype for an intelligent assistant that monitors adherence and provides feedback to the patient, and tested monitoring precision with healthy subjects for one strategy called a chin tuck.
636	The Malmo Platform for Artificial Intelligence Experimentation	Matthew Johnson, Katja Hofmann, Tim Hutton, David Bignell	We present Project Malmo – an AI experimentation platform built on top of the popular computer game Minecraft, and designed to support fundamental research in artificial intelligence.
637	A Demonstration of Interactive Task Learning	James Kirk, Aaron Mininger, John Laird	We will demonstrate a tabletop robotic agent that learns new tasks through interactive natural language instruction.
638	Implementation of Learning-Based Dynamic Demand Response on a Campus Micro-Grid	Sanmukh R. Kuppannagari, Rajgopal Kannan, Charalampos Chelmis, Viktor K. Prasanna	In this work, we demonstrate a system for a real time automated Dynamic DR (D2R).
639	Eddy: A Graphical Editor for OWL 2 Ontologies	Domenico Lembo, Daniele Pantaleone, Valerio Santarelli, Domenico Fabio Savo	We demonstrate Eddy, a new tool for designing ontologies specified in the Graphol language.
640	A Tag-Based Statistical English Math Word Problem Solver with Understanding, Reasoning and Explanation	Chao-Chun Liang, Kuang-Yi Hsu, Chien-Tsung Huang, Chung-Min Li, Shen-Yu Miao, Keh-Yih Su	Since the physical meaning of each quantity is explicitly represented with those tags and used in the inference process, the proposed approach could explain how the answer is obtained in a human comprehensible way.
641	An Intelligent System for Taxi Service Monitoring, Analytics and Visualization	Yu Lu, Gim Guan Chua, Huayu Wu, Clement Shi Qi Ong	In this paper, we present a novel and practical system for taxi service monitoring, analytics and visualization.
642	An Adaptive Process Management System Implementation Based on Situation Calculus, Indigolog and Classical Planning	Andrea Marrella, Massimo Mecella, Sebastian Sardina	In this paper, we introduce an adaptive Process Management System implementation that combines business process execution monitoring, unanticipated exception detection and automated resolution strategies leveraging on well-established formalisms developed for reasoning about actions in Artificial Intelligence, including the Situation Calculus, IndiGolog and classical planning.
643	Demo: Assisting Visually Impaired People Navigate Indoors	J. Pablo Muñoz, Bing Li, Xuejian Rong, Jizhong Xiao, Yingli Tian, Aries Arditi	Research in Artificial Intelligence, Robotics and Computer Vision has recently made great strides in improving indoor localization.
644	Klint: Assisting Integration of Heterogeneous Knowledge	Jacobo Rouces, Gerard de Melo, Katja Hose	This paper presents Klint, a web-based system that automatically creates mappings to transform knowledge as provided by the sources into data that conforms to a large unified schema.
645	Thou Shalt ASQFor and Shalt Receive the Semantic Answer	Muhammad Rizwan Saeed, Charalampos Chelmis, Viktor K. Prasanna	In this demonstration, we will present ASQFor, a systematic framework for automated SPARQL query formulation and execution over RDF repository using simple concept-based search primitives.
646	A Tool for Generating Interactive Euler Diagrams	François Schwarzentruber	We describe a tool for generating Euler diagrams from a set of region connection calculus formulas.
647	Interactive Planning-Based Hypothesis Generation with LTS++	Shirin Sohrabi, Octavian Udrea, Anton V. Riabov, Oktie Hassanzadeh	We present LTS++, an interactive development environment for planning-based hypothesis generation in applications with unreliable observations.
648	Data-Based Promotion of Tourist Events with Minimal Operational Impact	Srikanth Tamilselvam, Biplav Srivastava, Vishalaksh Aggarwal	We demonstrate a family of novel, standards-based, online applications for promoting tourist events with minimal operational impact using AI methods.
649	PARecommender: A Pattern-Based System for Route Recommendation	Feiyi Tang, Jia Zhu, Yang Cao, Sanli Ma, Yulong Chen, Jing He, Changqin Huang, Gansen Zhao, Yong Tang	In this demo, we present a system called PARecommender, which predicts traffic conditions and provides route recommendation based on generated traffic patterns.
650	VIPR: An Interactive Tool for Meaningful Visualization of High-Dimensional Data	Donghan Wang, Madalina Fiterau, Artur Dubrawski	In this demonstration, we present a powerful analysis tool that uses IPE methodology in support of fundamental machine learning tasks: regression, classification, and clustering.
651	Robot Scavenger Hunt: A Standardized Framework for Evaluating Intelligent Mobile Robots	Shiqi Zhang, Dongcai Lu, Xiaoping Chen, Peter Stone	The main goal of the Robot Scavenger Hunt is to provide a standardized framework that includes a set of standardized tasks for evaluating the AI and robotic capabilities of medium-sized intelligent mobile robots.