Paper Digest: IJCAI 2015 Highlights

July 24, 2015May 9, 2020 admin

International Joint Conference on Artificial Intelligence (IJCAI) is one of the top artificial intelligence conferences in the world. In 2015, it is to be held in Buenos Aires, Agentina.

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 2015 Papers

	Title	Authors	Highlight
1	Optimal Incremental Preference Elicitation during Negotiation	Tim Baarslag, Enrico H. Gerding	To this end, we propose a new model in which a negotiating agent may incrementally elicit the user’s preference during the negotiation.
2	Composing and Verifying Commitment-Based Multiagent Protocols	Matteo Baldoni, Cristina Baroglio, Amit K. Chopra, Munindar P. Singh	We consider the design and enactment of multiagent protocols that describe collaboration using "normative" or "social" abstractions, specifically, commitments.
3	Strategic Abstention Based on Preference Extensions: Positive Results and Computer-Generated Impossibilities	Florian Brandl, Felix Brandt, Christian Geist, Johannes Hofbauer	Our contribution is twofold.
4	Efficiency and Complexity of Price Competition Among Single-Product Vendors	Ioannis Caragiannis, Xenophon Chatzigeorgiou, Panagiotis Kanellopoulos, George A. Krimpas, Nikos Protopapas, Alexandros A. Voudouris	We model the behaviour of buyers and vendors as a two-stage full-information game and study a series of questions related to the existence, efficiency (price of anarchy) and computational complexity of equilibria in this game.
5	Generalizing the Single-Crossing Property on Lines and Trees to Intermediate Preferences on Median Graphs	Adam Clearwater, Clemens Puppe, Arkadii Slinko	We provide a polynomial-time algorithm to recognize whether or not a given profile is intermediate with respect to some median graph.
6	Learning Behaviors in Agents Systems with Interactive Dynamic Influence Diagrams	Ross Conroy, Yifeng Zeng, Marc Cavazza, Yingke Chen	In this paper, we use automatic techniques for learning behavior of other agents from replay data in RTS games.
7	Structural Results for Cooperative Decentralized Control Models	Jilles Steeve Dibangoye, Olivier Buffet, Olivier Simonin	This paper introduces a general methodology — structural analysis — for the design of optimality-preserving concise policies and value functions, which will eventually lead to the development of efficient theory and algorithms.
8	Tractable Inquiry in Information-Rich Environments	Barbara Dunin-Kęplicz, Alina Strachocka	In this paper we provide a paraconsistent and paracomplete implementation of inquiry dialogue under realistic assumptions regarding availability and quality of information.
9	An Adaptive Computational Model for Personalized Persuasion	Yilin Kang, Ah-Hwee Tan, Chunyan Miao	Grounded in the Elaboration Likelihood Model (ELM), this paper presents a computational model called Model for Adaptive Persuasion (MAP) for virtual agents.
10	Tradeoffs between Incentive Mechanisms in Boolean Games	Vadim Levit, Zohar Komarovsky, Tal Grinshpoun, Amnon Meisels	A distributed search algorithm for finding the side payments needed for securing a PNE is proposed.
11	Environment-Driven Social Force Model: Lévy Walk Pattern in Collective Behavior	Danyan Lv, Zhaofeng Li, Yichuan Jiang	Here, we propose an environment-driven social force model to simulate overall foraging process of an agent group.
12	The Power of Local Manipulation Strategies in Assignment Mechanisms	Timo Mennle, Michael Weiss, Basil Philipp, Sven Seuken	We consider three important, non-strategyproof assignment mechanisms: Probabilistic Serial and two variants of the Boston mechanism.
13	Revenue Maximization Envy-Free Pricing for Homogeneous Resources	Gianpiero Monaco, Piotr Sankowski, Qiang Zhang	One of the main goals in such studies is to avoid envy between the agents, i.e., guarantee fair allocation.
14	Exchange of Indivisible Objects with Asymmetry	Zhaohong Sun, Hideaki Hata, Taiki Todo, Makoto Yokoo	In this paper we study the exchange of indivisible objects where agents’ possible preferences over the objects are strict and share a common structure among all of them, which represents a certain level of asymmetry among objects.
15	Characterization of Scoring Rules with Distances: Application to the Clustering of Rankings	Paolo Viappiani	This work extends a previous known observation connecting Borda count with the minimization of the sum of the Spearman distances (calculated with respect to a set of input rankings).
16	Quantifying Robustness of Trust Systems against Collusive Unfair Rating Attacks Using Information Theory	Dongxia Wang, Tim Muller, Jie Zhang, Yang Liu	We improve on these results in multiple ways: (1) we alter the methodology to be able to reason about colluding attackers as well, and (2) we extend the method to be able to measure the strength of any attacks (rather than just the strongest attack).
17	Optimal Auctions for Partially Rational Bidders	Zihe Wang, Pingzhong Tang	We investigate the problem of revenue optimal mechanism design [Myerson, 1981] under the context of the partial rationality model, where buyers randomize between two modes: rational and irrational.
18	An Expert-Level Card Playing Agent Based on a Variant of Perfect Information Monte Carlo Sampling	Florian Wisser	We propose Presumed Value PIMC resolving the problem of overestimation of opponent’s knowledge of hidden information in future game states.
19	Agile Planning for Real-World Disaster Response	Feng Wu, Sarvapali D. Ramchurn, Wenchao Jiang, Jeol E. Fischer, Tom Rodden, Nicholas R. Jennings	Hence, we propose a new model re-planning problem using a Multi-agent Markov Decision Process that integrates potential rejections as part of the planning process and propose a novel algorithm to efficiently solve this new model.
20	Optimal Pricing for the Competitive and Evolutionary Cloud Market	Bolei Xu, Tao Qin, Guoping Qiu, Tie-Yan Liu	We study the problem of how to optimize a cloud service provider’s pricing policy so as to better compete with other providers.
21	Uncovering Hidden Structure through Parallel Problem Decomposition for the Set Basis Problem: Application to Materials Discovery	Yexiang Xue, Stefano Ermon, Carla P. Gomes, Bart Selman	We evaluate our approach on the minimum set basis problem: a core combinatorial problem with a range of applications in optimization, machine learning, and system security.
22	Emotions in Argumentation: an Empirical Evaluation	Sahbi Benlamine, Maher Chaouachi, Serena Villata, Elena Cabrio, Claude Frasson, Fabien Gandon	In this paper, we assess this claim by means of an experiment: during several debates people’s argumentation in plain English is connected and compared to the emotions automatically detected from the participants.
23	Parliamentary Voting Procedures: Agenda Control, Manipulation, and Uncertainty	Robert Bredereck, Jiehua Chen, Rolf Niedermeier, Toby Walsh	We study computational problems for two popular parliamentary voting procedures: the amendment procedure and the successive procedure.
24	Computer Science on the Move: Inferring Migration Regularities from the Web via Compressed Label Propagation	Fabian Hadiji, Martin Mladenov, Christian Bauckhage, Kristian Kersting	Consequently, we introduce compressed LP (CLP) that exploits these symmetries to reduce the dimensions of the matrix inverted by LP to obtain optimal labeling scores.
25	Semi-Universal Portfolios with Transaction Costs	Dingjiang Huang, Yan Zhu, Bin Li, Shuigeng Zhou, Steven C.H. Hoi	We present an efficient implementation of the strategy based on non-uniform random walks and online factor graph algorithms.
26	Context-Independent Claim Detection for Argument Mining	Marco Lippi, Paolo Torroni	We thus propose a method that exploits structured parsing information to detect claims without resorting to contextual information, and yet achieve a performance comparable to that of state-of-the-art methods that heavily rely on the context.
27	A Deterministic Partition Function Approximation for Exponential Random Graph Models	Wen Pu, Jaesik Choi, Yunseong Hwang, Eyal Amir	In this paper, we introduce a new quadratic time deterministic approximation to these partition functions.
28	Bonus or Not? Learn to Reward in Crowdsourcing	Ming Yin, Yiling Chen	We take an algorithmic approach to decide when to offer bonuses in a working session to improve the overall utility that a requester derives from the session.
29	Mechanism Design and Implementation for Lung Exchange	Suiqian Luo, Pingzhong Tang	To implement this mechanism in practice, we propose an algorithm based on Integer Linear Program and another based on search.
30	Maximal Cooperation in Repeated Games on Social Networks	Catherine Moon, Vincent Conitzer	In this paper, we consider games with cooperation and defection.
31	Selling Reserved Instances in Cloud Computing	Changjun Wang, Weidong Ma, Tao Qin, Xujin Chen, Xiaodong Hu, Tie-Yan Liu	In this paper, we study the problem of designing new mechanisms for selling reserved instances (al-so referred to as virtual machines) in cloud computing.
32	A Multicore Tool for Constraint Solving	Roberto Amadini, Maurizio Gabbrielli, Jacopo Mauro	In this paper we introduce sunny-cp2: the first parallel CP portfolio solver that enables a dynamic, cooperative, and simultaneous execution of its solvers in a multicore setting.
33	Improving the Effectiveness of SAT-Based Preprocessing for MaxSAT	Jeremias Berg, Paul Saikko, Matti Järvisalo	As a remedy, we propose a lifting of MaxHS that works directly on LCNFs, allowing for a tighter integration of SAT-based preprocessing and MaxHS.
34	Maximum Satisfiability Using Cores and Correction Sets	Nikolaj Bjorner, Nina Narodytska	In this work, we propose a new efficient algorithm that is guided by correction sets and cores.
35	Recursive Decomposition for Nonconvex Optimization	Abram L. Friesen, Pedro Domingos	Based on this, we propose a problem-decomposition approach to nonconvex optimization.
36	Finding Diverse Solutions of High Quality to Constraint Optimization Problems	Thierry Petit, Andrew C. Trapp	In this paper, we tackle this issue with a generic paradigm that can be implemented in most existing solvers.
37	On the Resiliency of Unit Propagation to Max-Resolution	André Abramé, Djamal Habet	In this paper, we study the effect of the transformations on the UP mechanism.
38	Improving the Efficiency of Dynamic Programming on Tree Decompositions via Machine Learning	Michael Abseher, Frederico Dusberger, Nysret Musliu, Stefan Woltran	We thus propose here a novel and general method that is based on a selection of the best decomposition from an available pool of heuristically generated ones.
39	Exploiting the Structure of Unsatisfiable Cores in MaxSAT	Carlos Ansotegui, Frederic Didier, Joel Gabas	We propose a new approach that exploits the good properties of core-guided and model-guided MaxSAT solvers.
40	Multi-Armed Bandits for Adaptive Constraint Propagation	Amine Balafrej, Christian Bessiere, Anastasia Paparrizou	In this work, we propose a simple learning technique, based on multi-armed bandits, that allows to automatically select among several levels of propagation during search.
41	Combining Preference Elicitation and Search in Multiobjective State-Space Graphs	Nawal Benabbou, Patrice Perny	The aim of this paper is to propose a new approach interweaving preference elicitation and search to solve multiobjective optimization problems.
42	ReACTR: Realtime Algorithm Configuration through Tournament Rankings	Tadhg Fitzgerald, Yuri Malitsky, Barry O’Sullivan	The enhancements to ReACT that we present enable us to even outperform existing static configurators like SMAC in a non-dynamic setting.
43	Expressive Logical Combinators for Free	Pierre Geneves, Alan Schmitt	We present logical combinators whose benefit is to provide an exponential gain in succinctness in terms of the size of the logical representation.
44	Statistical Regimes and Runtime Prediction	Barry Hurley, Barry O’Sullivan	Supported by a large-scale empirical study employing many years of industrial SAT Competition instances including repeated runs, we present statistical and empirical evidence that such a performance variation phenomenon necessitates a change in the evaluation of portfolio, runtime prediction, and automated configuration methods.
45	Solving QBF by Clause Selection	Mikolas Janota, Joao Marques-Silva	Algorithms based on the enumeration of implicit hitting sets find a growing number of applications, which include maximum satisfiability and model based diagnosis, among others.
46	Compiling Constraint Networks into Multivalued Decomposable Decision Graphs	Frédéric Koriche, Jean-Marie Lagniez, Pierre Marquis, Samuel Thomas	We present and evaluate a top-down algorithm for compiling finite-domain constraint networks (CNs) into the language MDDG of multivalued decomposable decision graphs.
47	Filtering Nogoods Lazily in Dynamic Symmetry Breaking During Search	Jimmy H. M. Lee, Zichen Zhu	In this paper, we propose weak-nogood consistency (WNC) for nogoods and a lazy propagator for SBDS (and its variants) using watched literal technology.
48	Multi-Pass High-Level Presolving	Kevin Leo, Guido Tack	We present an integrated approach that performs presolving as a separate pass during the compilation from high-level optimisation models to solver-level programs.
49	Decomposition of the Factor Encoding for CSPs	Chavalit Likitvivatanavong, Wei Xia, Roland H. C. Yap	We propose a variation of the FE that aims at reducing redundant columns in the constraints of the FE while still preserving full pairwise consistency.
50	Towards Automatic Dominance Breaking for Constraint Optimization Problems	Christopher Mears, Maria Garcia de la Banda	We propose an automatic method to detect some of the dominance relations manually identified by Chu and Stuckey for optimization problems, and to construct the associated dominance breaking constraints.
51	On the Empirical Time Complexity of Random 3-SAT at the Phase Transition	Zongxu Mu, Holger H. Hoos	After introducing a refined model for the location of the phase transition point, we show that the median running time of three incomplete, SLS-based solvers — WalkSAT/SKC, BalancedZ and probSAT — scales polynomially with instance size.
52	Efficient Operations On MDDs for Building Constraint Programming Models	Guillaume Perez, Jean-Charles Régin	We propose improved algorithms for defining the most common operations on Multi-Valued Decision Diagrams (MDDs): creation, reduction, complement, intersection, union, difference, symmetric difference, complement of union and complement of intersection.
53	Personalized Mathematical Word Problem Generation	Oleksandr Polozov, Eleanor O’Rourke, Adam M. Smith, Luke Zettlemoyer, Sumit Gulwani, Zoran Popović	We propose a novel technique for automatic generation of personalized word problems.
54	On Constrained Boolean Pareto Optimization	Chao Qian, Yang Yu, Zhi-Hua Zhou	This work theoretically compares Pareto optimization with a penalty approach, which is a common method transforming a constrained optimization into an unconstrained optimization.
55	Packing Curved Objects	Ignacio Antonio Salas Donoso, Gilles Chabert	The aim of this paper is to generalize the approach by replacing the ad-hoc formulas with a numerical algorithm that automatically measures the overlapping between two objects.
56	Efficient Algorithms with Performance Guarantees for the Stochastic Multiple-Choice Knapsack Problem	Long Tran-Thanh, Yingce Xia, Tao Qin, Nicholas R Jennings	In particular, we propose OPT-S-MCKP, the first algorithm that achieves optimality when the value-weight distributions are known.
57	Probabilistic Inference Based Message-Passing for Resource Constrained DCOPs	Supriyo Ghosh, Akshat Kumar, Pradeep Varakantham	We present a promising new class of algorithm for RC-DCOPs by translating the underlying coordination problem to probabilistic inference.
58	Collective Biobjective Optimization Algorithm for Parallel Test Paper Generation	Minh Luan Nguyen, Siu Cheung Hui, Alvis C. M. Fong	Parallel Test Paper Generation (k-TPG) is a biobjective distributed resource allocation problem, which aims to generate multiple similarly optimal test papers automatically according to multiple user-specified criteria.Generating high-quality parallel test papers is challenging due to its NP-hardness in maximizing the collective objective functions.In this paper, we propose a Collective Biobjective Optimization (CBO) algorithm for solving k-TPG.
59	Max-Sum Goes Private	Tamir Tassa, Roie Zivan, Tal Grinshpoun	As part of the ongoing effort of designing secure DCOP algorithms, we propose P-Max-Sum, the first private algorithm that is based on Max-Sum.
60	Applying Max-Sum to Asymmetric Distributed Constraint Optimization	Roie Zivan, Tomer Parash, Yarden Naveh	We study the adjustment and use of the Max-sumalgorithm for solving Asymmetric Distributed ConstraintOptimization Problems (ADCOPs).
61	A Bargaining Mechanism for One-Way Games	Andres Abeliuk, Gerardo Berbeglia, Pascal Van Hentenryck	We introduce one-way games, a framework motivated by applications in large-scale power restoration, humanitarian logistics, and integrated supply-chains.
62	Strategic Network Formation through an Intermediary	Elliot Anshelevich, Onkar Bhardwaj, Koushik Kar	We investigate the existence and worst-case efficiency (price of anarchy) of stable solutions in these settings, and especially when the intermediary uses common pricing schemes like proportional pricing or marginal cost pricing.
63	The Adjusted Winner Procedure: Characterizations and Equilibria	Haris Aziz, Simina Brânzei, Aris Filos-Ratsikas, Søren Kristoffer Stiil Frederiksen	We show that Adjusted Winner admits several elegant characterizations, which further shed light on the outcomes reached with strategic agents.
64	Welfare Maximization in Fractional Hedonic Games	Haris Aziz, Serge Gaspers, Joachim Gudmundsson, Julian Mestre, Hanjo Taubig	We present both intractability results and approximation algorithms for computing welfare maximizing partitions.
65	Possible and Necessary Allocations via Sequential Mechanisms	Haris Aziz, Toby Walsh, Lirong Xia	We present characterizations of the allocations that result respectively from the classes, which extend the well-known characterization by Brams and King [2005] for policies without restrictions.
66	Learning Cooperative Games	Maria Florina Balcan, Ariel D. Procaccia, Yair Zick	This paper explores a PAC (probably approximately correct) learning model in cooperative games.
67	A Dictatorship Theorem for Cake Cutting	Simina Brânzei, Peter Bro Miltersen	We consider discrete protocols for the classical Steinhaus cake cutting problem.
68	Simultaneous Abstraction and Equilibrium Finding in Games	Noam Brown, Tuomas Sandholm	We introduce a method that combines abstraction with equilibrium finding by enabling actions to be added to the abstraction at run time.
69	Incentivizing Peer Grading in MOOCS: An Audit Game Approach	Alejandro Uriel Carbonara, Anupam Datta, Arunesh Sinha, Yair Zick	We present the first model of strategic auditing in peer grading, modeling the student’s choice of effort in response to a grader’s audit levels as a Stackelberg game with multiple followers.
70	Approximate Nash Equilibria with Near Optimal Social Welfare	Artur Czumaj, Michail Fasoulakis, Marcin Jurdzinski	In this paper, we show that for every fixed ε > 0, every bimatrix game (with values in [0, 1]) has an ε-approximate Nash equilibrium with the total payoff of the players at least a constant factor, (1 − √1 − ε)2, of the optimum.
71	Influence in Classification via Cooperative Game Theory	Amit Datta, Anupam Datta, Ariel D. Procaccia, Yair Zick	In this work, we employ an axiomatic approach in order to uniquely characterize an influence measure: a function that, given a set of classified points, outputs a value for each feature corresponding to its influence in determining the classification outcome.
72	SAT Is an Effective and Complete Method for Solving Stable Matching Problems with Couples	Joanna Drummond, Andrew Perrault, Fahiem Bacchus	In this paper we examine stable matching problems arising from labour market with couples (SMP-C).
73	Optimal Network Security Hardening Using Attack Graph Games	Karel Durkota, Viliam Lisý, Branislav Bošanský, Christopher Kiekintveld	We introduce a new game-theoretic model of the interaction between a network administrator who uses limited resource to harden a network and an attacker who follows a multi-stage plan to attack the network.
74	Gibbard–Satterthwaite Games	Edith Elkind, Umberto Grandi, Francesca Rossi, Arkadii Slinko	In this paper, we ask what happens if a given profile admits several such voters.
75	Equilibrium Refinement through Negotiation in Binary Voting	Umberto Grandi, Davide Grossi, Paolo Turrini	We study voting games on binary issues, where voters might hold an objective over some issues at stake, while willing to strike deals on the remaining ones, and can influence one another’s voting decision before the vote takes place.
76	Structural Tractability of Shapley and Banzhaf Values in Allocation Games	Gianluigi Greco, Francesco Lupia, Francesco Scarcello	Structural Tractability of Shapley and Banzhaf Values in Allocation Games
77	Smooth UCT Search in Computer Poker	Johannes Heinrich, David Silver	In this paper we introduce Smooth UCT, a variant of the established Upper Confidence Bounds Applied to Trees (UCT) algorithm.
78	Fixing Tournaments for Kings, Chokers, and More	Michael P. Kim, Virginia Vassilevska Williams	We study the tournament fixing problem (TFP), which asks whether a tournament organizer can rig a single-elimination (SE) tournament such that their favorite player wins, simply by adjusting the initial seeding.
79	A Characterization of n-Player Strongly Monotone Scheduling Mechanisms	Annamaria Kovacs, Angelina Vidali	We characterize truthful mechanisms for n players and 2 tasks or items, as either task-independent, or a player-grouping minimizer, a new class of mechanisms we discover, which generalizes affine minimizers.
80	Limited Lookahead in Imperfect-Information Games	Christian Kroer, Tuomas Sandholm	This paper initiates a new direction via two simultaneous deviation points: generalization to imperfect-information games and a game-theoretic approach.
81	Impartial Peer Review	David Kurokawa, Omer Lev, Jamie Morgenstern, Ariel D. Procaccia	Motivated by a radically new peer review system that the National Science Foundation recently experimented with, we study peer review systems in which proposals are reviewed by PIs who have submitted proposals themselves.
82	Truthful Cake Cutting Mechanisms with Externalities: Do Not Make Them Care for Others Too Much!	Minming Li, Jialin Zhang, Qiang Zhang	We propose and study the following model: given an allocation, externalities of agents are modeled as percentages of the reported values that other agents have for their pieces.
83	Equilibrium Analysis of Multi-Defender Security Games	Jian Lou, Yevgeniy Vorobeychik	We present the analysis of three models of increasing generality, two in which each defender protects multiple targets.
84	When Does Schwartz Conjecture Hold?	Matthias Mnich, Yash Raj Shrestha, Yongjie Yang	In this paper, we prove sufficient conditions for infinite classes of tournaments that satisfy Schwartz’s Conjecture and Brandt’s Conjecture.
85	Strategic Candidacy Games with Lazy Candidates	Svetlana Obraztsova, Edith Elkind, Maria Polukarov, Zinovi Rabinovich	In this work, we extend the standard model of strategic candidacy games by observing that candidates may find it costly to run an electoral campaign and may therefore prefer to withdraw if their presence has no effect on the election outcome.
86	Simple Causes of Complexity in Hedonic Games	Dominik Peters, Edith Elkind	In this paper, we identify simple conditions on expressivity of hedonic games that are sufficient for the problem of checking whether a given game admits a stable outcome to be computationally hard.
87	Convergence to Equilibria in Strategic Candidacy	Maria Polukarov, Svetlana Obraztsova, Zinovi Rabinovich, Alexander Kruglyi, Nicholas R. Jennings	Focusing on games under Plurality, we extend the standard model to allow for situations where voters may refuse to return their votes to those candidates who had previously left the election, should they decide to run again.
88	A Pseudo-Polynomial Algorithm for Computing Power Indices in Graph-Restricted Weighted Voting Games	Oskar Skibski, Tomasz P. Michalak, Yuko Sakurai, Makoto Yokoo	In this paper, we study the time complexity of computing two well-known power indices — the Shapley-Shubik index and the Banzhaf index – in the graph-restricted weighted voting games.
89	The Game-Theoretic Interaction Index on Social Networks with Applications to Link Prediction and Community Detection	Piotr Lech Szczepański, Aleksy Stanisław Barcz, Tomasz Paweł Michalak, Talal Rahwan	In this paper, we construct anew measure of similarity between nodes based on the game-theoretic interaction index (Grabisch and Roubens, 1997).
90	Solving Heads-Up Limit Texas Hold’em	Oskari Tammelin, Neil Burch, Michael Johanson, Michael Bowling	In this paper we describe in detail the engineering details required to make this computation a reality.
91	Envy-Free Sponsored Search Auctions with Budgets	Bo Tang, Jinshan Zhang	Our primary goal is to design auctions that maximize social welfare and revenue — two classical objectives in auction theory.
92	Implementing the Wisdom of Waze	Shoshana Vasserman, Michal Feldman, Avinatan Hassidim	We study a setting of non-atomic routing in a network of m parallel links with asymmetry of information.
93	Spiteful Bidding in the Dollar Auction	Marcin Waniek, Agata Nieścieruk, Tomasz Michalak, Talal Rahwan	In this paper, inspired by the recent literature on spiteful bidders, we ask whether the escalation in the dollar auction can be induced by meanness.
94	Security Games with Information Leakage: Modeling and Computation	Haifeng Xu, Albert Xing Jiang, Arunesh Sinha, Zinovi Rabinovich, Shaddin Dughmi, Milind Tambe	More specifically, after describing the information leakage model, we start with an LP formulation to compute the defender’s optimal strategy in the presence of leakage.
95	Computing Optimal Mixed Strategies for Security Games with Dynamic Payoffs	Yue Yin, Haifeng Xu, Jiarui Gan, Bo An, Albert Xin Jiang	We propose an optimal algorithm and an arbitrarily near-optimal algorithm to compute security strategies under different conditions.
96	From Weighted to Unweighted Model Counting	Supratik Chakraborty, Dror Fried, Kuldeep S. Meel, Moshe Y. Vardi	In this paper, we present a new approach to weighted model counting via reduction to unweighted model counting.
97	Pushing Forward Marginal MAP with Best-First Search	Radu Marinescu, Rina Dechter, Alexander Ihler	In order to minimize the number of likelihood evaluations, we focus in this paper on best-first search strategies for exploring the space of partial MAP assignments.
98	Indirect Causes in Dynamic Bayesian Networks Revisited	Alexander Motzek, Ralf Möller	By introducing activator random variables, we propose template fragments for modeling dynamic Bayesian networks under a causal use of time, anticipating indirect influences on a solid mathematical basis, obeying the laws of Bayesian networks.
99	Differential Semantics of Intervention in Bayesian Networks	Biao Qin	In this paper, we reveal the connection between differentiation and intervention in Bayesian networks.
100	Bayesian Modelling of Community-Based Multidimensional Trust in Participatory Sensing under Data Sparsity	Matteo Venanzi, Luke Teacy, Alex Rogers, Nick Jennings	We propose a new Bayesian model for reliable aggregation of crowdsourced estimates of real-valued quantities in participatory sensing applications.
101	A Unified Model for Unsupervised Opinion Spamming Detection Incorporating Text Generality	Yinqing Xu, Bei Shi, Wentao Tian, Wai Lam	We have proposed a unified probabilistic graphical model to detect the suspicious review spams, the review spammers and the manipulated offerings in an unsupervised manner.
102	Model-Based Genetic Algorithms for Algorithm Configuration	Carlos Ansotegui, Yuri Malitsky, Horst Samulowitz, Meinolf Sellmann, Kevin Tierney	We introduce a new model designed specifically for the task of predicting high-performance regions in the parameter space.
103	ICBS: Improved Conflict-Based Search Algorithm for Multi-Agent Pathfinding	Eli Boyarski, Ariel Felner, Roni Stern, Guni Sharon, David Tolpin, Oded Betzalel, Eyal Shimony	This paper introduces two new improvements to CBS and incorporates them into a coherent, improved version of CBS, namely ICBS.
104	Balance between Complexity and Quality: Local Search for Minimum Vertex Cover in Massive Graphs	Shaowei Cai	In this paper, we propose a simple and fast local search algorithms called FastVC for solving MinVC in massive graphs, which is based on two low-complexity heuristics.
105	Generalized Rapid Action Value Estimation	Tristan Cazenave	We propose to generalize the RAVE heuristic so as to have more accurate estimates near the leaves.
106	A Fast Goal Recognition Technique Based on Interaction Estimates	Yolanda E-Martin, Maria D. R-Moreno, David E. Smith	In this paper, we introduce an approach that propagates cost and interaction information in a plan graph, and uses this information to estimate goal probabilities.
107	Interplanetary Trajectory Planning with Monte Carlo Tree Search	Daniel Hennes, Dario Izzo	In this work, we present a heuristic-free approach to automated trajectory planning (including the encounter sequence planning) based on Monte Carlo Tree Search (MCTS).
108	FlashNormalize: Programming by Examples for Text Normalization	Dileep Kini, Sumit Gulwani	We propose to learn programs for such normalization tasks through examples.
109	Efficient Search with an Ensemble of Heuristics	Mike Phillips, Venkatraman Narayanan, Sandip Aine, Maxim Likhachev	In this paper, we present two principled methods to adaptively distribute computation time among the different searches of the Multi- Heuristic A* algorithm.
110	Compositional Program Synthesis from Natural Language and Examples	Mohammad Raza, Sumit Gulwani, Natasa Milic-Frayling	In this paper we propose a new approach to end-user program synthesis in which input can be given in a compositional manner through a combination of natural language and examples.
111	Mining Expert Play to Guide Monte Carlo Search in the Opening Moves of Go	Erik S. Steinmetz, Maria Gini	We propose a method to guide a Monte Carlo search in the initial moves of the game of Go.
112	H-Index Manipulation by Merging Articles: Models, Theory, and Experiments	René van Bevern, Christian Komusiewicz, Rolf Niedermeier, Manuel Sorge, Toby Walsh	Herein, to model realistic manipulation scenarios, we define a compatability graph whose edges correspond to plausible merges.
113	Computing Possibly Optimal Solutions for Multi-Objective Constraint Optimisation with Tradeoffs	Nic Wilson, Abdul Razak, Radu Marinescu	We develop an AND/OR Branch-and-Bound algorithm for computing the set of Possibly Optimal solutions, and compare variants of the algorithm experimentally.
114	Mining Definitions from RDF Annotations Using Formal Concept Analysis	Mehwish Alam, Aleksey Buzmakov, Victor Codocedo, Amedeo Napoli	The popularization and quick growth of Linked Open Data (LOD) has led to challenging aspects regarding quality assessment and data exploration of the RDF triples that shape the LOD cloud.Particularly, we are interested in the completeness of data and its potential to provide concept definitions in terms of necessary and sufficient conditions.In this work we propose a novel technique based on Formal Concept Analysis which organizes RDF data into a concept lattice.This allows data exploration as well as the discovery of implications, which are used to automatically detect missing information and then to complete RDF data.Moreover, this is a way of reconciling syntax and semantics in the LOD cloud.Finally, experiments on the DBpedia knowledge base show that the approach is well-founded and effective.
115	Personalizing Product Rankings Using Collaborative Filtering on Opinion-Derived Topic Profiles	Claudiu Cristian Musat, Boi Faltings	We propose switching between a personalized and a non personalized method based on the user opinion profile.
116	AskWorld: Budget-Sensitive Query Evaluation for Knowledge-on-Demand	Mehdi Samadi, Partha Talukdar, Manuela Veloso, Tom Mitchell	In this paper, we address the problem of knowledge integration for on-demand time-budgeted query answering.
117	Building Hierarchies of Concepts via Crowdsourcing	Yuyin Sun, Adish Singla, Dieter Fox, Andreas Krause	In this paper, we propose a crowdsourcing system to build a hierarchy and furthermore capture the underlying uncertainty.
118	Finite Abstractions for the Verification of Epistemic Properties in Open Multi-Agent Systems	Francesco Belardinelli, Davide Grossi, Alessio Lomuscio	We study the verification problem of these systems and show that, under specific conditions, finite bisimilar abstractions can be obtained.
119	Formal Analysis of Dialogues on Infinite Argumentation Frameworks	Francesco Belardinelli, Davide Grossi, Nicolas Maudet	We develop a formal model for representing such dialogues, and introduce FO A -ATL, a first-order extension of alternating-time logic, for expressing the interplay of strategic and argumentation-theoretic properties.
120	On the Graded Acceptability of Arguments	Davide Grossi, Sanjay Modgil	The paper develops a formal theory of the degree of justification of arguments, which relies solely on the structure of an argumentation framework.
121	A Common-Sense Conceptual Categorization System Integrating Heterogeneous Proxytypes and the Dual Process of Reasoning	Antonio Lieto, Daniele Paolo Radicioni, Valentina Rho	In this article we present DUAL-PECCS, an integrated Knowledge Representation system aimed at extending artificial capabilities in tasks such as conceptual categorization.
122	A Simple Probabilistic Extension of Modal Mu-calculus	Wanwei Liu, Lei Song, Ji Wang, Lijun Zhang	In this paper, we present a natural and succinct probabilistic extension of Mu-calculus, another prominent logic in the concurrency theory.
123	The Complexity of MAP Inference in Bayesian Networks Specified Through Logical Languages	Denis Deratani Maua, Cassio Polpo de Campos, Fabio Gagliardi Cozman	We study the computational complexity of finding maximum a posteriori configurations in Bayesian networks whose probabilities are specified by logical formulas.
124	Dissecting German Grammar and Swiss Passports: Open-Domain Decomposition of Compositional Entries in Large-Scale Knowledge Repositories	Marius Pasca, Hylke Buisman	This paper presents a weakly supervised method that decomposes potentially compositional topics (Swiss passport) into zero or more constituent topics (Switzerland, Passport), where all topics are entries in a knowledge repository.
125	Automatic Generation of Raven’s Progressive Matrices	Ke Wang, Zhendong Su	Our goal is to efficiently generate a large number of RPMs that are authentic (i.e. similar to manually written problems), interesting (i.e. diverse in terms of difficulty), and well-formed (i.e unambiguous).
126	From Raw Sensor Data to Detailed Spatial Knowledge	Peng Zhang, Jae Hee Lee, Jochen Renz	We present a method for extracting detailed spatial information from sensor measurements of regions.
127	Using a Recursive Neural Network to Learn an Agent’s Decision Model for Plan Recognition	Francis Bisson, Hugo Larochelle, Froduald Kabanza	In this paper, we present a recursive neural network model that learns such a decision model automatically.
128	Biclustering Gene Expressions Using Factor Graphs and the Max-Sum Algorithm	Matteo Denitto, Alessandro Farinelli, Manuele Bicego	In this paper we present a novel approach to gene expression biclustering by providing a binary Factor Graph formulation to such problem.
129	Greedy Structure Search for Sum-Product Networks	Aaron Dennis, Dan Ventura	In this paper we introduce an algorithm that learns the structure of an SPN using a greedy search approach.
130	On the Consistency of AUC Pairwise Optimization	Wei Gao, Zhi-Hua Zhou	In this paper, we introduce the generalized calibration for AUC optimization, and prove that it is a necessary condition for AUC consistency.
131	Multi-Label Active Learning: Query Type Matters	Sheng-Jun Huang, Songcan Chen, Zhi-Hua Zhou	In this paper, we disclose for the first time that the query type, which decides what information to query for the selected instance, is more important.
132	Joint Learning of Constituency and Dependency Grammars by Decomposed Cross-Lingual Induction	Wenbin Jiang, Qun Liu, Thepchai Supnithi	We propose a decomposed projection strategy for cross-lingual induction, where cross-lingual projection is performed in unit of fundamental decisions of the structured predication.
133	Mobility Profiling for User Verification with Anonymized Location Data	Miao Lin, Hong Cao, Vincent Zheng, Kevin Chen-Chuan Chang, Shonali Krishnaswamy	Unlike the current methods that commonly require users active cooperation, such as entering a short pin or a one-stroke draw pattern, we propose a new passive verification method that requires minimal imposition of users through modelling users subtle mobility patterns.
134	Analysis of Sampling Algorithms for Twitter	Deepan Subrahmanian Palguna, Vikas Joshi, Venkatesan Chakaravarthy, Ravi Kothari, LV Subramaniam	In this work we come up with a theoretical formulation for sampling Twitter data.
135	Portfolio Choices with Orthogonal Bandit Learning	Weiwei Shen, Jun Wang, Yu-Gang Jiang, Hongyuan Zha	In this paper, we present a bandit algorithm for conducting online portfolio choices by effectually exploiting correlations among multiple arms.
136	Information Gathering in Networks via Active Exploration	Adish Singla, Eric Horvitz, Pushmeet Kohli, Ryen White, Andreas Krause	In contrast, we propose a novel model where we start our exploration from an initial node, and new nodes become visible and available for selection only once one of their neighbors has been chosen.
137	Medical Synonym Extraction with Concept Space Models	Chang Wang, Liangliang Cao, Bowen Zhou	In this paper, we present a novel approach for medical synonym extraction.
138	Detecting Emotions in Social Media: A Constrained Optimization Approach	Yichen Wang, Aditya Pal	In this paper, we propose a constraint optimization framework to discover emotions from social media content of the users.
139	Regression Model Fitting under Differential Privacy and Model Inversion Attack	Yue Wang, Cheng Si, Xintao Wu	In this paper, we develop a novel approach which leverages the functional mechanism to perturb coefficients of the polynomial representation of the objective function but effectively balances the privacy budget for sensitive and non-sensitive attributes in learning the differential privacy preserving regression model.
140	Correcting Covariate Shift with the Frank-Wolfe Algorithm	Junfeng Wen, Russell Greiner, Dale Schuurmans	In this paper, using inspiration from recent optimization techniques, we apply the Frank-Wolfe algorithm to two well-known covariate shift correction techniques, Kernel Mean Matching (KMM) and Kullback-Leibler Importance Estimation Procedure (KLIEP), and identify an important connection between kernel herding and KMM.
141	Cognitive Modelling for Predicting Examinee Performance	Runze Wu, Qi Liu, Yuping Liu, Enhong Chen, Yu Su, Zhigang Chen, Guoping Hu	To this end, we propose a fuzzy cognitive diagnosis framework (FuzzyCDF) for examinees’ cognitive modelling with both objective and subjective problems.
142	Opportunities or Risks to Reduce Labor in Crowdsourcing Translation? Characterizing Cost versus Quality via a PageRank-HITS Hybrid Model	Rui Yan, Yiping Song, Cheng-Te Li, Ming Zhang, Xiaohua Hu	In this paper, we propose a graph-based PageRank-HITS Hybrid model to distinguish authoritative workers from unreliable ones.
143	Auxiliary Information Regularized Machine for Multiple Modality Feature Learning	Yang Yang, Han-Jia Ye, De-Chuan Zhan, Yuan Jiang	In this paper, we point out that different modalities should be treated with different strategies and propose the Auxiliary information Regularized Machine (ARM), which works by extracting the most discriminative feature subspace of weak modality while regularizing the strong modal predictor.
144	Discriminative Reordering Model Adaptation via Structural Learning	Biao Zhang, Jinsong Su, Deyi Xiong, Hong Duan, Junfeng Yao	In this paper, we propose a novel adaptive discriminative reordering model (DRM) based on structural learning, which can capture correspondences among reordering features from two different domains.
145	Revisiting Gaussian Process Dynamical Models	Jing Zhao, Shiliang Sun	In this paper, we present four new algorithms (MAP+, Fix.α⁺, B-GPDM+ and T.MAP+) for learning GPDMs with incomplete training data and a new conditional model (CM+) for recovering incomplete test data.
146	Character-Based Parsing with Convolutional Neural Network	Xiaoqing Zheng, Haoyuan Peng, Yi Chen, Pengjing Zhang, Wenqiang Zhang	We describe a novel convolutional neural network architecture with k-max pooling layer that is able to successfully recover the structure of Chinese sentences.
147	Active Learning from Crowds with Unsure Option	Jinhong Zhong, Ke Tang, Zhi-Hua Zhou	We propose the ALCU-SVM algorithm for this new learning problem.
148	Symbolic Model Checking for One-Resource RB+-ATL	Natasha Alechina, Brian Logan, Hoang Nga Nguyen, Franco Raimondi	In this paper, we consider a fragment of RB+-ATL, 1RB+-ATL, that allows only one resource type.
149	The Complexity of Model Checking Succinct Multiagent Systems	Xiaowei Huang, Qingliang Chen, Kaile Su	This paper studies the complexity of model checking multiagent systems, in particular systems succinctly described by two practical representations: concurrent representation and symbolic representation.
150	Verifying Emergent Properties of Swarms	Panagiotis Kouvaros, Alessio Lomuscio	We introduce a sound and complete procedure for solving the problem.
151	Pushdown Multi-Agent System Verification	Aniello Murano, Giuseppe Perelli	In this paper we investigate the model-checking problem of pushdown multi-agent systems for ATL* specifications.To this aim, we introduce pushdown game structures over which ATL* formulas are interpreted.
152	A Scalable Interdependent Multi-Issue Negotiation Protocol for Energy Exchange	Muddasser Alam, Enrico H. Gerding, Alex Rogers, Sarvapali D. Ramchurn	We present a novel negotiation protocol to facilitate energy exchange between off-grid homes that are equipped with renewable energy generation and electricity storage.
153	Equilibria Under the Probabilistic Serial Rule	Haris Aziz, Serge Gaspers, Simon Mackenzie, Nicholas Mattei, Nina Narodytska, Toby Walsh	In this work, we target at the problem of offline sketch parsing, in which the temporal orders of strokes are unavailable.
154	Towards City-Scale Mobile Crowdsourcing: Task Recommendations under Trajectory Uncertainties	Cen Chen, Shih-Fen Cheng, Hoong Chuin Lau, Archan Misra	In this work, we investigate the problem of large-scale mobile crowdsourcing, where workers are financially motivated to perform location-based tasks physically.
155	Estimating the Margin of Victory of an Election Using Sampling	Palash Dey, Y. Narahari	In this work, we present efficient sampling based algorithms for estimating the margin of victory of elections.
156	Strategy-Proofness of Scoring Allocation Correspondences for Indivisible Goods	Nhan-Tam Nguyen, Dorothea Baumeister, Jörg Rothe	We study resource allocation in a model due to Brams and King [2005] and further developed by Baumeister et al. [2014].
157	Spectrum-Based Fault Localisation for Multi-Agent Systems	Lúcio S. Passos, Rui Abreu, Rosaldo J. F. Rossetti	In this paper, we propose a light-weight, automatic debugging-based technique, coined ESFL-MAS, which shortens the diagnostic process, while only relying on minimal information about the system.
158	What Do We Elect Committees For? A Voting Committee Model for Multi-Winner Rules	Piotr Krzysztof Skowron	We present a new model that describes the process of electing a group of representatives (e.g., a parliament) for a group of voters.
159	Algorithmic Exam Generation	Omer Geiger, Shaul Markovitch	In this work we present a novel algorithmic framework for exam composition.
160	The Right to Obscure: A Mechanism and Initial Evaluation	Eric Hsin-Chun Huang, Jaron Lanier, Yoav Shoham	Instead of the right to be forgotten, we propose the right to obscure certain facts about oneself on search engines, and a simple mechanism which respects the spirit of the ruling by giving people more power to influence search results for queries on their names.
161	A New Input Method for Human Translators: Integrating Machine Translation Effectively and Imperceptibly	Guoping Huang, Jiajun Zhang, Yu Zhou, Chengqing Zong	In this paper, we propose a novel approach deeply integrating MT into CAT systems: a well-designed input method which makes full use of the knowledge adopted by MT systems, such as translation rules, decoding hypotheses and n-best translation lists.
162	Combining Eye Movements and EEG to Enhance Emotion Recognition	Yifei Lu, Wei-Long Zheng, Binbin Li, Bao-Liang Lu	In this paper, we adopt a multimodal emotion recognition framework by combining eye movements and electroencephalography (EEG) to enhance emotion recognition.
163	Handling Complex Commands as Service Robot Task Requests	Vittorio Perera, Manuela Veloso	We introduce a flexible template-based algorithm to extract such structure from the parse tree of the sentence.
164	A Study of Human-Agent Collaboration for Multi-UAV Task Allocation in Dynamic Environments	Sarvapali D. Ramchurn, Joel E Fischer, Yuki Ikuno, Feng Wu, Jack Flann, Antony Waldock	Hence, we develop a set of multi-UAV supervisory control interfaces and a multi-agent coordination algorithm to support human decision making in this setting.
165	Automated Geometry Theorem Proving for Human-Readable Proofs	Ke Wang, Zhendong Su	This paper tackles these weaknesses of prior systems by introducing a geometry proof system, iGeoTutor, capable of generating human-readable elementary proofs, i.e. proofs using standard Euclidean axioms.
166	Offline Sketch Parsing via Shapeness Estimation	Jie Wu, Changhu Wang, Liqing Zhang, Yong Rui	In this work, we target at the problem of offline sketch parsing, in which the temporal orders of strokes are unavailable.
167	Multi-Document Abstractive Summarization Using ILP Based Multi-Sentence Compression	Siddhartha Banerjee, Prasenjit Mitra, Kazunari Sugiyama	In this work, we aim at developing an abstractive summarizer.
168	Do We Criticise (and Laugh) in the Same Way? Automatic Detection of Multi-Lingual Satirical News in Twitter	Francesco Barbieri, Francesco Ronzano, Horacio Saggion	In this paper we investigate the automatic detection of Tweets that advertise satirical news in English, Spanish and Italian.
169	Embedding Semantic Relations into Word Representations	Danushka Bollegala, Takanori Maehara, Ken-ichi Kawarabayashi	Learning representations for semantic relations is important for various tasks such as analogy detection, relational search, and relation classification.Although there have been several proposals for learning representations for individual words,learning word representations that explicitly capture the semantic relations between words remains under developed.We propose an unsupervised method for learning vector representations for words such that the learnt representations are sensitive to the semantic relations that exist between two words.First, we extract lexical patterns from the co-occurrence contexts of two words in a corpus to represent the semantic relations that exist between those two words.Second, we represent a lexical pattern as the weighted sum of the representations of the words that co-occur with that lexical pattern.
170	Positive, Negative, or Neutral: Learning an Expanded Opinion Lexicon from Emoticon-Annotated Tweets	Felipe Bravo-Marquez, Eibe Frank, Bernhard Pfahringer	We present a supervised framework for expanding an opinion lexicon for tweets.
171	Joint Learning of Character and Word Embeddings	Xinxiong Chen, Lei Xu, Zhiyuan Liu, Maosong Sun, Huanbo Luan	Hence, we take Chinese for example, and present a character-enhanced word embedding model (CWE).
172	A Hybrid Neural Model for Type Classification of Entity Mentions	Li Dong, Furu Wei, Hong Sun, Ming Zhou, Ke Xu	This paper introduces a hybrid neural model which classifies entity mentions to a wide-coverage set of 22 types derived from DBpedia.
173	Iterative Learning of Parallel Lexicons and Phrases from Non-Parallel Corpora	Meiping Dong, Yang Liu, Huanbo Luan, Maosong Sun, Tatsuya Izuha, Dakun Zhang	In this work, we propose a joint model for iteratively learning parallel lexicons and phrases from nonparallel corpora.
174	Word-Error Correction of Continuous Speech Recognition Based on Normalized Relevance Distance	Yohei Fusayasu, Katsuyuki Tanaka, Tetsuya Takiguchi, Yasuo Ariki	In this paper, we focus on a word-error correction system for continuous speech recognition using confusion networks.Conventional N-gram correction is widely used; however, the performance degrades due to the fact that the N-gram approach cannot measure information between long distance words.
175	Joint POS Tagging and Text Normalization for Informal Text	Chen Li, Yang Liu	In this paper, we propose a joint Viterbi decoding process to determine each token’s POS tag and non-standard token’s correct form at the same time. In order to evaluate our approach, we create two new data sets with POS tag labels and non-standard tokens’ correct forms.
176	Reader-Aware Multi-Document Summarization via Sparse Coding	Piji Li, Lidong Bing, Wai Lam, Hang Li, Yi Liao	To tackle this RA-MDS problem, we propose a sparse-coding-based method that is able to calculate the salience of the text units by jointly considering news reports and reader comments. In this work, we also generate a data set for conducting RA-MDS.
177	Incorporating Domain and Sentiment Supervision in Representation Learning for Domain Adaptation	Biao Liu, Minlie Huang, Jiashen Sun, Xuan Zhu	In this work, we address two key factors to guide the representation learning process for domain adaptation of sentiment classification — one is domain supervision, enforcing the learned representation to better predict the domain of an input, and the other is sentiment supervision which utilizes the source domain sentiment labels to learn sentiment-favorable representations.
178	Learning Context-Sensitive Word Embeddings with Neural Tensor Skip-Gram Model	Pengfei Liu, Xipeng Qiu, Xuanjing Huang	In this paper, we distinguish the different senses of each word by their latent topics.
179	Automated Rule Selection for Aspect Extraction in Opinion Mining	Qian Liu, Zhiqiang Gao, Bing Liu, Yuanlin Zhang	In this paper, we propose a novel method to select an effective set of rules.
180	Integrating Importance, Non-Redundancy and Coherence in Graph-Based Extractive Summarization	Daraksha Parveen, Michael Strube	We propose a graph-based method for extractive single-document summarization which considers importance, non-redundancy and local coherence simultaneously.
181	Convolutional Neural Tensor Network Architecture for Community-Based Question Answering	Xipeng Qiu, Xuanjing Huang	In this paper, we propose a convolutional neural tensor network architecture to encode the sentences in semantic space and model their interactions with a tensor layer.
182	An Active Learning Approach to Coreference Resolution	Mrinmaya Sachan, Eduard Hovy, Eric P. Xing	In this paper, we define the problem of coreference resolution in text as one of clustering with pairwise constraints where human experts are asked to provide pairwise constraints (pairwise judgments of coreferentiality) to guide the clustering process.
183	Towards Addressing the Winograd Schema Challenge — Building and Using a Semantic Parser and a Knowledge Hunting Module	Arpit Sharma, Nguyen H Vo, Somak Aditya, Chitta Baral	In this paper we demonstrate our progress towards addressing the WSC.
184	On Conceptual Labeling of a Bag of Words	Xiangyan Sun, Yanghua Xiao, Haixun Wang, Wei Wang	In this paper, we introduce the task of conceptual labeling (CL), which aims at generating a minimum set of conceptual labels that best summarize a bag of words.
185	Modeling Mention, Context and Entity with Neural Networks for Entity Disambiguation	Yaming Sun, Lei Lin, Duyu Tang, Nan Yang, Zhenzhou Ji, Xiaolong Wang	Given a query consisting of a mention (name string) and a background document,entity disambiguation calls for linking the mention to an entity from reference knowledge base like Wikipedia.Existing studies typically use hand-crafted features to represent mention, context and entity, which is labor-intensive and weak to discover explanatory factors of data.In this paper, we address this problem by presenting a new neural network approach.The model takes consideration of the semantic representations of mention, context and entity, encodes them in continuous vector space and effectively leverages them for entity disambiguation.Specifically, we model variable-sized contexts with convolutional neural network, and embed the positions of context words to factor in the distance between context word and mention.Furthermore, we employ neural tensor network to model the semantic interactions between context and mention.We conduct experiments for entity disambiguation on two benchmark datasets from TAC-KBP 2009 and 2010.
186	User Modeling with Neural Network for Review Rating Prediction	Duyu Tang, Bing Qin, Ting Liu, Yuekui Yang	The intuition is to factor in user-specific modification to the meaning of a certain word.Specifically, we extend the lexical semantic composition models and introduce a user-word composition vector model (UWCVM), which effectively captures how user acts as a function affecting the continuous word representation.
187	Target-Dependent Twitter Sentiment Classification with Rich Automatic Features	Tin Duy Vo, Yue Zhang	In this paper, we show that competitive results can be achieved without the use of syntax, by extracting a rich set of automatic features.
188	Syntax-Based Deep Matching of Short Texts	Mingxuan Wang, Zhengdong Lu, Hang Li, Qun Liu	We propose a new approach to the problem, called Deep Match Tree (DeepMatch_tree), under a general setting.
189	Modeling Quantum Entanglements in Quantum Language Models	Mengjiao Xie, Yuexian Hou, Peng Zhang, Jingfei Li, Wenjie Li, Dawei Song	Recently, a Quantum Language Model (QLM) was proposed to model term dependencies upon Quantum Theory (QT) framework and successively applied in Information Retrieval (IR).
190	Convolutional Neural Networks for Text Hashing	Jiaming Xu, Peng Wang, Guanhua Tian, Bo Xu, Jun Zhao, Fangyuan Wang, Hongwei Hao	Here we propose a novel text hashing framework with convolutional neural networks.
191	Compressive Document Summarization via Sparse Optimization	Jin-ge Yao, Xiaojun Wan, Jianguo Xiao	In this paper, we formulate a sparse optimization framework for extractive document summarization.
192	Optimizing Sentence Modeling and Selection for Document Summarization	Wenpeng Yin, Yulong Pei	This paper attempts to build a strong summarizer DivSelect+CNNLM by presenting new algorithms to optimize each of them.
193	Learning Term Embeddings for Hypernymy Identification	Zheng Yu, Haixun Wang, Xuemin Lin, Min Wang	In this paper, we propose a simple yet effective supervision framework to identify hypernymy relations using distributed term representations (a.k.a term embeddings).
194	Local Translation Prediction with Global Sentence Representation	Jiajun Zhang, Dakun Zhang, Jie Hao	In this paper, we explore the source-side global sentence-level features for target-side local translation prediction.
195	Prior-Based Dual Additive Latent Dirichlet Allocation for User-Item Connected Documents	Wei Zhang, Jianyong Wang	In this paper, we propose a novel probabilistic topic model called Prior-based Dual Additive Latent Dirichlet Allocation (PDA-LDA).
196	Representation Learning for Measuring Entity Relatedness with Rich Information	Yu Zhao, Zhiyuan Liu, Maosong Sun	In this paper, we aim at incorporating multiple types of relations to measure the semantic relatedness between Wikipedia entities.
197	Linking Heterogeneous Input Features with Pivots for Domain Adaptation	Guangyou Zhou, Tingting He, Wensheng Wu, Xiaohua Tony Hu	In this paper, we propose to link heterogeneous input features with pivots via joint non-negative matrix factorization.
198	A Subspace Learning Framework for Cross-Lingual Sentiment Classification with Partial Parallel Data	Guangyou Zhou, Tingting He, Jun Zhao, Wensheng Wu	In this paper, we propose a novel subspace learning framework by leveraging the partial parallel data for cross-lingual sentiment classification.
199	Unsupervised Learning of an IS-A Taxonomy from a Limited Domain-Specific Corpus	Daniele Alfarone, Jesse Davis	This paper proposes a novel, unsupervised algorithm for automatically learning an IS-A taxonomy from scratch by analyzing a given text corpus.
200	Coherence Across Components in Cognitive Systems — One Ontology to Rule Them All	Gregor Behnke, Denis Ponomaryov, Marvin Schiller, Pascal Bercher, Florian Nothdurft, Birte Glimm, Susanne Biundo	An approach is presented that exploits a single source of common knowledge contained in an ontology.
201	How to Select One Preferred Assertional-Based Repair from Inconsistent and Prioritized DL-Lite Knowledge Bases?	Salem Benferhat, Zied Bouraoui, Karim Tabia	It proposes new approaches based on the selection of only one preferred repair.
202	Scalable Maintenance of Knowledge Discovery in an Ontology Stream	Freddy Lecue	Scalable Maintenance of Knowledge Discovery in an Ontology Stream
203	Bootstrapping Domain Ontologies from Wikipedia: A Uniform Approach	Daniil Mirylenka, Andrea Passerini, Luciano Serafini	We propose an automatic method for bootstrapping domain ontologies from the categories of Wikipedia.
204	An Ontology Matching Approach Based on Affinity-Preserving Random Walks	Chuncheng Xiang, Baobao Chang, Zhifang Sui	In this paper, we propose a novel graph-based approach to ontology matching problem.
205	Exploiting Symmetries by Planning for a Descriptive Quotient	Mohammad Abdulaziz, Michael Norrish, Charles Gretton	Exploiting Symmetries by Planning for a Descriptive Quotient
206	Cost-Optimal and Net-Benefit Planning — A Parameterised Complexity View	Meysam Aghighi, Christer Bäckström	We thus apply parameterised complexity analysis to shed more light on this issue.
207	On the Boundary of (Un)decidability: Decidable Model-Checking for a Fragment of Resource Agent Logic	Natasha Alechina, Nils Bulling, Brian Logan, Hoang Nga Nguyen	We review existing (un)decidability results and identify a significant fragment of the logic for which model checking is decidable.
208	Tight Bounds for HTN Planning with Task Insertion	Ron Alford, Pascal Bercher, David W. Aha	Tight Bounds for HTN Planning with Task Insertion
209	ASAP-UCT: Abstraction of State-Action Pairs in UCT	Ankit Anand, Aditya Grover, Mausam, Parag Singla	This paper makes two contributions.
210	Further Connections Between Contract-Scheduling and Ray-Searching Problems	Spyros Angelopoulos	Our objective is to demonstrate that several well-motivated settings can be addressed using a common approach.
211	Temporal Planning with Semantic Attachment of Non-Linear Monotonic Continuous Behaviours	Josef Bajada, Maria Fox, Derek Long	We present an algorithm which builds upon existent temporal planning techniques based on linear programming to approximate non-linear continuous monotonic functions.
212	A Privacy Preserving Algorithm for Multi-Agent Planning and Search	Ronen Israel Brafman	The main contribution of this paper is an enhanced version of the distributed forward-search planning framework of Nissim and Brafman that reveals less information than the original algorithm, and the first, to our knowledge, discussion and formal proof of privacy guarantees for distributed planning and search algorithms.
213	Exploiting Block Deordering for Improving Planners Efficiency	Lukáš Chrpa, Fazlul Hasan Siddiqui	In this paper, we introduce a method, called BloMa, that learns domain-specific macros from plans, decomposed into macro-blocks which are extensions of blocks, utilising structural knowledge they capture.
214	On the Online Generation of Effective Macro-Operators	Lukáš Chrpa, Mauro Vallati, Thomas Leo McCluskey	In this paper we propose OMA, an efficient method for generating useful macros without an offline learning phase, by utilising lessons learnt from existing macro learning techniques.
215	Estimating the Probability of Meeting a Deadline in Hierarchical Plans	Liat Cohen, Solomon Eyal Shimony, Gera Weiss	We provide a polynomial-time approximation algorithm for it.
216	Synthesis for LTL and LDL on Finite Traces	Giuseppe De Giacomo, Moshe Vardi	In this paper, we study synthesis from logical specifications over finite traces expressed in LTLf and its extension LDLf.
217	Mixed Discrete-Continuous Heuristic Generative Planning Based on Flow Tubes	Enrique Fernandez-Gonzalez, Erez Karpas, Brian C. Williams	We introduce Scotty, a mixed discrete-continuous generative planner that finds the middle ground between these two.
218	Delete Relaxations for Planning with State-Dependent Action Costs	Florian Geißer, Thomas Keller, Robert Mattmüller	We evaluate these approaches theoretically and present an implementation of the additive heuristic for planning with state-dependent action costs.
219	Optimal Planning with Axioms	Franc Ivankovic, Patrik Haslum	We consider axioms in the form of a logic program with recursively defined predicates and negation-as-failure, as in PDDL 2.2.
220	Optimal Policy Generation for Partially Satisfiable Co-Safe LTL Specifications	Bruno Lacerda, David Parker, Nick Hawes	We present a method to calculate cost-optimal policies for co-safe linear temporal logic task specifications over a Markov decision process model of a stochastic system.
221	Probabilistic Knowledge-Based Programs	Jérôme Lang, Bruno Zanuttini	We introduce Probabilistic Knowledge-Based Programs (PKBPs), a new, compact representation of policies for factored partially observable Markov decision processes.
222	Metareasoning for Planning Under Uncertainty	Christopher H. Lin, Andrey Kolobov, Ece Kamar, Eric Horvitz	We present approximate metareasoning procedures which rely on special properties of the BRTDP planning algorithm and explore the effectiveness of our methods on a variety of problems.
223	Classical Planning with Simulators: Results on the Atari Video Games	Nir Lipovetzky, Miquel Ramirez, Hector Geffner	The empirical results over 54 Atari games show that the simplest such algorithm performs at the level of UCT, the state-of-the-art planning method in this domain, and suggest the potential of width-based methods for planning with simulators when factored, compact action models are not available.
224	Action2Activity: Recognizing Complex Activities from Sensor Data	Ye Liu, Liqiang Nie, Lei Han, Luming Zhang, David S. Rosenblum	To this end, this paper presents a novel approach for complex activity recognition comprising of two components.
225	Exploratory Digraph Navigation Using A*	Fabrice Mayran de Chamisso, Laurent Soulier, Michaël Aupetit	We describe Exploratory Digraph Navigation as a fundamental problem of graph theory concerned with using a graph with incomplete edge and vertex information for navigation in a partially unknown environment.
226	Compiling Away Uncertainty in Strong Temporal Planning with Uncontrollable Durations	Andrea Micheli, Minh Do, David E. Smith	In this paper, we describe a sound-and-complete compilation technique for strong planning that reduces any planning instance with uncertainty in the duration of actions to a plain temporal planning problem without uncertainty.
227	Sorting Sequential Portfolios in Automated Planning	Sergio Núñez, Daniel Borrajo, Carlos Linares López	We propose to sort the component solvers in a sequential portfolio, such that the resulting ordered portfolio maximizes the probability of providing the largest performance at any point in time.
228	Factored Upper Bounds for Multiagent Planning Problems under Uncertainty with Non-Factored Value Functions	Frans Adriaan Oliehoek, Matthijs T. J. Spaan, Stefan John Witwicki	To mitigate this problem, this paper introduces a family of influence-optimistic upper bounds for factored Dec-POMDPs without factored value functions.
229	Adversarial Hierarchical-Task Network Planning for Complex Real-Time Games	Santiago Ontanon, Michael Buro	This paper presents an alternative approach called Adversarial Hierarchical Task Network (AHTN) planning that combines ideas from game tree search with HTN planning.
230	Models of Action Concurrency in Temporal Planning	Jussi Rintanen	Models of temporal planning are complex, due to the possibility of multiple concurrent and mutually interacting actions.This work compares two modeling languages, one with a PDDL-style action exclusion mechanism, and another with an explicit notion of resources, and investigates their implications on constraint-based search.
231	Point-Based Planning for Multi-Objective POMDPs	Diederik Marijn Roijers, Shimon Whiteson, Frans A. Oliehoek	We propose optimistic linear support with alpha reuse (OLSAR), which computes a bounded approximation of the optimal solution set for all possible weightings of the objectives.
232	Deordering and Numeric Macro Actions for Plan Repair	Enrico Scala, Pietro Torasso	The paper faces the problem of plan repair in presence of numeric information, by providing a new method for the intelligent selection of numeric macro actions.
233	Planning for Stochastic Games with Co-Safe Objectives	Lei Song, Yuan Feng, Lijun Zhang	The paper faces the problem of plan repair in presence of numeric information, by providing a new method for the intelligent selection of numeric macro actions.
234	Simulation-Based Admissible Dominance Pruning	álvaro Torralba, Jörg Hoffmann	We apply simulation, well-known in model checking, to compute much more general dominance relations based on comparing transition behavior across states.
235	Polynomial-Time Reformulations of LTL Temporally Extended Goals into Final-State Goals	Jorge Torres, Jorge A. Baier	In this paper, we present a polynomial approach to compiling away LTL goals.
236	On the Effective Configuration of Planning Domain Models	Mauro Vallati, Frank Hutter, Lukas Chrpa, Thomas Leo McCluskey	In this paper, we investigate how the performance of planners is affected by domain model configuration.
237	Integrating Partial Order Reduction and Symmetry Elimination for Cost-Optimal Classical Planning	Martin Wehrle, Malte Helmert, Alexander Shleyfman, Michael Katz	In this paper, we propose safe integrations of partial order reduction and symmetry elimination for cost-optimal classical planning.
238	Multi-Objective POMDPs with Lexicographic Reward Preferences	Kyle Hollins Wray, Shlomo Zilberstein	We propose a model, Lexicographic Partially Observable Markov Decision Process (LPOMDP), which extends POMDPs with lexicographic preferences over multiple value functions.
239	An Iterative Approach to Synthesize Data Transformation Programs	Bo Wu, Craig A. Knoblock	An Iterative Approach to Synthesize Data Transformation Programs
240	MORRF*: Sampling-Based Multi-Objective Motion Planning	Daqing Yi, Michael A. Goodrich, Kevin D Seppi	We present a theoretical analysis that demonstrates that the algorithm asymptotically produces the set of Pareto optimal solutions, and use simulations to demonstrate the effectiveness and efficiency of MORRF* in approximating the Pareto set.
241	Optimal Greedy Diversity for Recommendation	Azin Ashkan, Branislav Kveton, Shlomo Berkovsky, Zheng Wen	In this work, we propose a novel approach to diversifying a list of recommended items, which maximizes the utility of the items subject to the increase in their diversity.
242	Music Recommenders: User Evaluation Without Real Users?	Susan Craw, Ben Horsburgh, Stewart Massie	This paper presents a low cost method that leverages available social media data and shows it to be effective.
243	A Synthetic Approach for Recommendation: Combining Ratings, Social Relations, and Reviews	Guang-Neng Hu, Xin-Yu Dai, Yunya Song, Shu-Jian Huang, Jia-Jun Chen	In this paper, we investigate the effective data fusion by combining the two approaches, in two steps.
244	Differentially Private Matrix Factorization	Jingyu Hua, Chang Xia, Sheng Zhong	This paper proposes a differentially private MF mechanism that can prevent an untrusted recommender from learning any users’ ratings or profiles.
245	Sparse Probabilistic Matrix Factorization by Laplace Distribution for Collaborative Filtering	Liping Jing, Peng Wang, Liu Yang	In this work, we propose a sparse probabilistic matrix factorization method (SPMF) by utilizing a Laplacian distribution to model the item/user factor vector.
246	Personalized Tour Recommendation Based on User Interests and Points of Interest Visit Durations	Kwan Hui Lim, Jeffrey Chan, Christopher Leckie, Shanika Karunasekera	We propose an algorithm called PersTour for recommending personalized tours using POI popularity and user interest preferences, which are automatically derived from real-life travel sequences based on geo-tagged photos.
247	Modeling Users’ Dynamic Preference for Personalized Recommendation	Xin Liu	In this paper, for implicit feedback data, we propose a personalized recommendation model to capture users’ dynamic preference using Gaussian process.
248	A Boosting Algorithm for Item Recommendation with Implicit Feedback	Yong Liu, Peilin Zhao, Aixin Sun, Chunyan Miao	In this paper, we propose a boosting algorithm named AdaBPR (Adaptive Boosting Personalized Ranking) for top-N item recommendation using users’ implicit feedback.
249	Simple Atom Selection Strategy for Greedy Matrix Completion	Zebang Shen, Hui Qian, Tengfei Zhou, Song Wang	In this paper we focus on the greedy matrix completion problem.
250	Personalized Ad Recommendation Systems for Life-Time Value Optimization with Guarantees	Georgios Theocharous, Philip S. Thomas, Mohammad Ghavamzadeh	In this paper, we propose a framework for using reinforcement learning (RL) algorithms to learn good policies for personalized ad recommendation (PAR) systems.
251	Exploring Implicit Hierarchical Structures for Recommender Systems	Suhang Wang, Jiliang Tang, Yilin Wang, Huan Liu	In this paper, we investigate the problem of exploring the implicit hierarchical structures for recommender systems when they are not explicitly available.
252	Recommendation Algorithms for Optimizing Hit Rate, User Satisfaction and Website Revenue	Xin Wang, Yunhui Guo, Congfu Xu	In this paper, we propose two algorithms for the above purposes and design two modified hit rate based metrics to measure them.
253	Cross-Domain Collaborative Filtering with Review Text	Xin Xin, Zhirun Liu, Chin-Yew Lin, Heyan Huang, Xiaochi Wei, Ping Guo	In this paper, we investigate how to utilize the review text to improve cross-domain collaborative filtering models.
254	Inducing Probabilistic Relational Rules from Probabilistic Examples	Luc De Raedt, Anton Dries, Ingo Thon, Guy Van den Broeck, Mathias Verbeke	We study the problem of inducing logic programs in a probabilistic setting, in which both the example descriptions and their classification can be probabilistic.
255	Saul: Towards Declarative Learning Based Programming	Parisa Kordjamshidi, Dan Roth, Hao Wu	We present Saul, a new probabilistic programming language designed to address some of the shortcomings of programming languages that aim at advancing and simplifying the development of AI systems.
256	Anytime Inference in Probabilistic Logic Programs with Tp-Compilation	Jonas Vlasselaer, Guy Van den Broeck, Angelika Kimmig, Wannes Meert, Luc De Raedt	We propose Tp-compilation, a new inference technique based on forward reasoning.
257	Knowledge Base Completion Using Embeddings and Rules	Quan Wang, Bin Wang, Li Guo	This paper proposes a novel approach which incorporates rules seamlessly into embedding models for KB completion.
258	Toward Estimating Others’ Transition Models Under Occlusion for Multi-Robot IRL	Kenneth Bogert, Prashant Doshi	Challenged by occlusion where large portions of others’ state spaces are fully hidden, we present a new approach that maps stochastic transitions to distributions over features.
259	Graph-Based Inverse Optimal Control for Robot Manipulation	Arunkumar Byravan, Mathew Monfort, Brian Ziebart, Byron Boots, Dieter Fox	We address the problem of using IOC in these computationally challenging control tasks by using a graph-based discretization of the trajectory space.
260	Reactive Integrated Motion Planning and Execution	Andreas G. Hofmann, Enrique Fernandez, Justin Helbert, Scott D. Smith, Brian C. Williams	We present Chekhov, a reactive, integrated motion planning and execution system that addresses these problems.
261	Weakly Supervised RBM for Semantic Segmentation	Yong Li, Jing Liu, Yuhang Wang, Hanqing Lu, Songde Ma	In this paper, we propose a weakly supervised Restricted Boltzmann Machines (WRBM) approach to deal with the task of semantic segmentation with only image-level labels available.
262	Grounding the Meaning of Words through Vision and Interactive Gameplay	Natalie Parde, Adam Hair, Michalis Papakostas, Konstantinos Tsiakas, Maria Dagioglou, Vangelis Karkaletsis, Rodney D. Nielsen	This work presents a means to satisfy that need, by abstracting the task of training robots to learn about the world around them as a vision- and dialogue-based game, I Spy.
263	Intelligent Agent Supporting Human-Multi-Robot Team Collaboration	Ariel Rosenfeld, Noa Agmon, Oleg Maksimov, Amos Azaria, Sarit Kraus	In this paper we propose a novel approach for utilizing advising automated agents when assisting an operator to better manage a team of multiple robots in complex environments. We introduce the Myopic Advice Optimization (MYAO) Problem and exemplify its implementation using an agent for the Search And Rescue (SAR) task.
264	Co-Acquisition of Syntax and Semantics — An Investigation in Spatial Language	Michael Spranger, Luc Steels	This paper reports recent progress on modeling the grounded co-acquisition of syntax and semantics of locative spatial language in developmental robots.
265	Reduced Time-Expansion Graphs and Goal Decomposition for Solving Cooperative Path Finding Sub-Optimally	Pavel Surynek	In this paper, we propose a reduced time expansion that is focused on makespan sub-optimal solving.
266	Learning to Interpret Natural Language Commands through Human-Robot Dialog	Jesse Thomason, Shiqi Zhang, Raymond J Mooney, Peter Stone	We introduce a dialog agent for mobile robots that understands human instructions through semantic parsing, actively resolves ambiguities using a dialog manager, and incrementally learns from human-robot conversations by inducing training data from user paraphrases.
267	Logic-Geometric Programming: An Optimization-Based Approach to Combined Task and Motion Planning	Marc Toussaint	We propose to formulate the problem holistically as a 1st-order logic extension of a mathematical program: a non-linear constrained program over the full world trajectory where the symbolic state-action sequence defines the (in-)equality constraints.
268	Multi-Modality Tracker Aggregation: From Generative to Discriminative	Xiaoqin Zhang, Wei Li, Mingyu Fan, Di Wang, Xiuzi Ye	This paper proposes a multi-modality ranking aggregation framework for fusion of multiple tracking algorithms.
269	Tractable Classes of Binary CSPs Defined by Excluded Topological Minors	David A. Cohen, Martin C. Cooper, Peter G Jeavons, Stanislav Zivny	In this paper we introduce the notion of a topological minor of a binary CSP instance.
270	A Modularity-Based Random SAT Instances Generator	Jesús Giráldez-Cru, Jordi Levy	In this paper, we use modularity, or community structure, to define a new model of pseudo-industrial random SAT instances, called Community Attachment.
271	An Exact Inference Scheme for MinSAT	Chu-Min Li, Felip Manyà	We describe an exact inference-based algorithm for the MinSAT problem.
272	Efficient Model Based Diagnosis with Maximum Satisfiability	Joao Marques-Silva, Mikoláš Janota, Alexey Ignatiev, Antonio Morgado	This paper proposes a novel encoding of MBD into maximum satisfiability (MaxSAT). The paper also proposes a new set of challenging MBD instances, which can be used for evaluating new MBD approaches.
273	Literal-Based MCS Extraction	Carlos Mencía, Alessandro Previti, Joao Marques-Silva	This paper builds on earlier work and proposes a finer-grained view of the MCS extraction problem, one that reasons in terms of literals instead of clauses.
274	Prime Compilation of Non-Clausal Formulae	Alessandro Previti, Alexey Ignatiev, Antonio Morgado, Joao Marques-Silva	This paper describes two novel approaches for the compilation of non-clausal formulae either with prime implicants or implicates, that is based on propositional Satisfiability (SAT) solving.
275	Solving MDPs with Skew Symmetric Bilinear Utility Functions	Hugo Gilbert, Olivier Spanjaard, Paolo Viappiani, Paul Weng	In this paper we adopt Skew Symmetric Bilinear (SSB) utility functions to compare policies in Markov Decision Processes (MDPs).
276	Non-Monotone Adaptive Submodular Maximization	Alkis Gotovos, Amin Karbasi, Andreas Krause	We propose the adaptive random greedy policy for maximizing adaptive submodular functions, and prove that it retains the aforementioned 1-1/e approximation ratio for functions that are also adaptive monotone, while it additionally provides a 1/e approximation ratio for non-monotone adaptive submodular functions.
277	Optimization of Probabilistic Argumentation with Markov Decision Models	Emmanuel Hadoux, Aurélie Beynier, Nicolas Maudet, Paul Weng, Anthony Hunter	In this paper we investigate the problem, for an agent, of optimizing a sequence of moves to be put forward in a debate, against an opponent assumed to behave stochastically, and equipped with an unknown initial belief state.
278	Stick-Breaking Policy Learning in Dec-POMDPs	Miao Liu, Christopher Amato, Xuejun Liao, Lawrence Carin, Jonathan P. How	This paper considers a variable-size FSC to represent the local policy of each agent.
279	Structure in Dichotomous Preferences	Edith Elkind, Martin Lackner	The goal of this paper is to extend this line of research to the setting where voters’ preferences are dichotomous, i.e., each voter approves a subset of candidates and disapproves the remaining candidates.
280	Efficient, Private, and eps-Strategyproof Elicitation of Tournament Voting Rules	David Timothy Lee	In this paper, we give algorithms which elicit approximate winners in a way which provably satisfies all three of these requirements simultaneously.
281	Lie on the Fly: Iterative Voting Center with Manipulative Voters	Lihi Naamani-Dery, Svetlana Obraztsova, Zinovi Rabinovich, Meir Kalech	In this paper, we combine two approaches of iterative processes: iterative preference elicitation and iterative voting and study the outcome and performance of a setting where manipulative voters submit partial preferences.
282	Ranked Voting on Social Networks	Ariel D. Procaccia, Nisarg Shah, Eric Sodomka	We establish a general framework — based on random utility theory — for ranked voting on a social network with arbitrarily many alternatives (in contrast to previous work, which is restricted to two alternatives).
283	Non-Myopic Negotiators See What’s Best	Yair Zick, Yoram Bachrach, Ian A. Kash, Peter Key	We consider revenue negotiation problems in iterative settings.
284	How Robust Is the Wisdom of the Crowds?	Noga Alon, Michal Feldman, Omer Lev, Moshe Tennenholtz	We introduce the study of adversarial effects on wisdom of the crowd phenomena.
285	Uncovering the Formation of Triadic Closure in Social Networks	Zhanpeng Fang, Jie Tang	In this paper, we study an interesting problem of decoding triadic closure in social networks.
286	Personalized Ranking Metric Embedding for Next New POI Recommendation	Shanshan Feng, Xutao Li, Yifeng Zeng, Gao Cong, Yeow Meng Chee, Quan Yuan	We propose a personalized ranking metric embedding method (PRME) to model personalized check-in sequences.
287	Influence Maximization in Big Networks: An Incremental Algorithm for Streaming Subgraph Influence Spread Estimation	Wei-Xue Lu, Peng Zhang, Chuan Zhou, Chunyi Liu, Li Gao	In this paper, we propose an incremental algorithm for influence spread estimation in big networks.
288	Nonnegative Matrix Tri-Factorization with Graph Regularization for Community Detection in Social Networks	Yulong Pei, Nilanjan Chakraborty, Katia Sycara	In this paper, we study the problem of detecting communities by combining social relations and user generated content in social networks.
289	A Scalable Community Detection Algorithm for Large Graphs Using Stochastic Block Models	Chengbin Peng, Zhihua Zhang, Ka-Chun Wong, Xiangliang Zhang, David Keyes	In this paper, we propose a multi-stage maximum likelihood approach to recover the latent parameters of the stochastic block model, in time linear with respect to the number of edges.
290	CEIL: A Scalable, Resolution Limit Free Approach for Detecting Communities in Large Networks	Vishnu Sankar, Balaraman Ravindran, Shivashankar S	In this paper, we argue that the popular scoring functions suffer from certain limitations.
291	Maximizing the Coverage of Information Propagation in Social Networks	Zhefeng Wang, Enhong Chen, Qi Liu, Yu Yang, Yong Ge, Biao Chang	Along this line, in this paper we propose a new problem called Information Coverage Maximization that aims to maximize the expected number of both active nodes and informed ones.
292	Network Representation Learning with Rich Text Information	Cheng Yang, Zhiyuan Liu, Deli Zhao, Maosong Sun, Edward Chang	By proving that DeepWalk, a state-of-the-art network representation method, is actually equivalent to matrix factorization (MF), we propose text-associated DeepWalk (TADW).
293	Optimal Route Search with the Coverage of Users’ Preferences	Yifeng Zeng, Xuefeng Chen, Xin Cao, Shengchao Qin, Marc Cavazza, Yanping Xiang	In this paper, we take into account the weighted user preferences in route search, and present a keyword coverage problem, which finds an optimal route from a source location to a target location such that the keyword coverage is optimized and that the budget score satisfies a specified constraint.
294	Integrated Anchor and Social Link Predictions across Social Networks	Jiawei Zhang, Philip S. Yu	In this paper, we want to predict the formation of social links among users in the target network as well as anchor links aligning the target network with other external social networks.
295	Groupwise Registration of Aerial Images	Ognjen Arandjelovic, Duc-Son Pham, Svetha Venkatesh	Our work introduces several novelties: (i) unlike all previous work on aerial image registration, we approach the problem using a set-based paradigm; (ii) we show how local, pair-wise constraints can be used to enforce a globally good registration using a constraints graph structure; (iii) we show how a simple holistic representation derived from raw aerial images can be used as a basic building block of the constraints graph in a manner which achieves both high registration accuracy and speed.
296	Video Covariance Matrix Logarithm for Human Action Recognition in Videos	Piotr Bilinski, Francois Bremond	In this paper, we propose a new local spatio-temporal descriptor for videos and we propose a new approach for action recognition in videos based on the introduced descriptor.
297	Modeling Inter- and Intra-Part Deformations for Object Structure Parsing	Ling Cai, Rongrong Ji, Wei Liu, Gang Hua	To this end, we propose a novel structure parsing model to capture deformable object structures.
298	Cross-View Projective Dictionary Learning for Person Re-Identification	Sheng Li, Ming Shao, Yun Fu	To improve the representation power of features, we learn discriminative and robust representations via dictionary learning in this paper.
299	Inferring Painting Style with Multi-Task Dictionary Learning	Gaowen Liu, Yan Yan, Elisa Ricci, Yi Yang, Yahong Han, Stefan Winkler, Nicu Sebe	In this paper we propose a novel dictionary learning approach to automatically uncover the artistic style from paintings. To demonstrate the effectiveness of our approach, we introduce the DART dataset, containing more than 1.5K images of paintings representative of different styles.
300	Social Image Parsing by Cross-Modal Data Refinement	Zhiwu Lu, Xin Gao, Songfang Huang, Liwei Wang, Ji-Rong Wen	This paper presents a cross-modal data refinement algorithm for social image parsing, or segmenting all the objects within a social image and then identifying their categories.
301	Salient Object Detection via Augmented Hypotheses	Tam Van Nguyen, Jose Sepulveda	In this paper, we propose using augmented hypotheses which consider objectness, foreground and compactness for salient object detection.
302	Adaptive Sharing for Image Classification	Li Shen, Gang Sun, Zhouchen Lin, Qingming Huang, Enhua Wu	In this paper, we formulate the image classification problem in a multi-task learning framework.
303	Face Clustering in Videos with Proportion Prior	Zhiqiang Tang, Yifan Zhang, Zechao Li, Hanqing Lu	In this paper, we investigate the problem of face clustering in real-world videos.
304	Trailer Generation via a Point Process-Based Visual Attractiveness Model	Hongteng Xu, Yi Zhen, Hongyuan Zha	In this work, we study the problem of automatic trailer generation, in which an attractive trailer is produced given a video and a piece of music.
305	Generalized Transitive Distance with Minimum Spanning Random Forest	Zhiding Yu, Weiyang Liu, Wenbo Liu, Xi Peng, Zhuo Hui, B. V. K. Vijaya Kumar	We show that such distance metric can be generalized onto a minimum spanning random forest (MSRF) with element-wise max pooling over the set of transitive distance matrices from an MSRF.
306	Saliency Detection with a Deeper Investigation of Light Field	Jun Zhang, Meng Wang, Jun Gao, Yi Wang, Xudong Zhang, Xindong Wu	In this work, we propose a new saliency detection model with light field data.
307	Semantic Single Video Segmentation with Robust Graph Representation	Handong Zhao, Yun Fu	In this paper, we propose an approach to semantically segment the multi-class foreground objects from a single video sequence. To achieve this, we firstly generate a set of proposals for each frame and score them based on motion and appearance features.
308	Reasoning with Style	Marti Bosch, Pierre Geneves, Nabil Layaida	We propose a set of automated refactoring techniques aimed at removing redundant and inaccessible declarations and rules, without affecting the layout of any document to which the style sheet is applied.
309	Semantic Concept Discovery for Large-Scale Zero-Shot Event Detection	Xiaojun Chang, Yi Yang, Alexander Hauptmann, Eric P Xing, Yao-Liang Yu	To address the challenging optimization formulation, we propose an efficient, highly scalable algorithm that is an order of magnitude faster than existing alternatives.
310	Raising Expectations in GDA Agents Acting in Dynamic Environments	Dustin Dannenhauer, Hector Munoz-Avila	In this paper we present a relaxation of this limitation.
311	Scalable Graph Hashing with Feature Transformation	Qing-Yuan Jiang, Wu-Jun Li	In this paper, we propose a novel method, called scalable graph hashing with feature transformation (SGH), for large-scale graph hashing.
312	Web Page Classification Based on Uncorrelated Semi-Supervised Intra-View and Inter-View Manifold Discriminant Feature Extraction	Xiao-Yuan Jing, Qian Liu, Fei Wu, Baowen Xu, Yangping Zhu, Songcan Chen	To our knowledge, only one method is specially presented for this topic.
313	Distance-Bounded Consistent Query Answering	Andreas Pfandler, Emanuel Sallinger	In this work we present a new approach where this distance is bounded and analyze its computational complexity.
314	Short and Sparse Text Topic Modeling via Self-Aggregation	Xiaojun Quan, Chunyu Kit, Yong Ge, Sinno Jialin Pan	In this paper, we present a novel model towards this goal by integrating topic modeling with short text aggregation during topic inference.
315	Personalized Sentiment Classification Based on Latent Individuality of Microblog Users	Kaisong Song, Shi Feng, Wei Gao, Daling Wang, Ge Yu, Kam-Fai Wong	In this paper, we propose a novel, extensible personalized sentiment classification method based on a variant of latent factor model to capture personal sentiment variations by mapping users and posts into a low-dimensional factor space.
316	Online Learning to Rank for Content-Based Image Retrieval	Ji Wan, Pengcheng Wu, Steven C. H. Hoi, Peilin Zhao, Xingyu Gao, Dayong Wang, Yongdong Zhang, Jintao Li	In this paper, we investigate a new framework of learning to rank for CBIR, which aims to seek the optimal combination of different retrieval schemes by learning from large-scale training data in CBIR.
317	Deep Multimodal Hashing with Orthogonal Regularization	Daixin Wang, Peng Cui, Mingdong Ou, Wenwu Zhu	In this paper, we propose a novel deep multimodal hashing method, namely Deep Multimodal Hashing with Orthogonal Regularization (DMHOR), which fully exploits intra-modality and inter-modality correlations.
318	Hamming Compatible Quantization for Hashing	Zhe Wang, Ling-Yu Duan, Jie Lin, Xiaofang Wang, Tiejun Huang, Wen Gao	In this paper, we propose a multi-bit quantization method named Hamming Compatible Quantization (HCQ) to preserve the capability of similarity metric between Euclidean space and Hamming space by utilizing the neighborhood structure of raw data.
319	Determining Expert Research Areas with Multi-Instance Learning of Hierarchical Multi-Label Classification Model	Tao Wu, Qifan Wang, Zhiwei Zhang, Luo Si	This paper proposes a novel approach, Multi-instance Learning of Hierarchical Multi-label Classification Model (MIHML) for the task, which effectively identifies multiple research areas in a hierarchy from individual documents within the profile of a researcher.
320	Sampling with Minimum Sum of Squared Similarities for Nystrom-Based Large Scale Spectral Clustering	Djallel Bouneffouf, Inanc Birol	This paper proposes an improved Nystrom-based clustering algorithm with a new sampling procedure, Minimum Sum of Squared Similarities (MSSS).
321	Tracking Political Elections on Social Media: Applications and Experience	Danish Contractor, Bhupesh Chawda, Sameep Mehta, L Venkata Subramaniam, Tanveer Afzal Faruquie	In this paper we describe our work for analyzing election campaigns using social media data.
322	Deep Learning for Event-Driven Stock Prediction	Xiao Ding, Yue Zhang, Ting Liu, Junwen Duan	We propose a deep learning method for event-driven stock market prediction.
323	Large Scale Homophily Analysis in Twitter Using a Twixonomy	Stefano Faralli, Giovanni Stilo, Paola Velardi	In this paper we perform a large-scale homophily analysis on Twitter using a hierarchical representation of users’ interests which we call a Twixonomy.
324	Interactive Gender Inference with Integer Linear Programming	Shoushan Li, Jingjing Wang, Guodong Zhou, Hanxiao Shi	In this paper, we address this task by proposing a joint inference approach which well incorporates label correlations among the instances.
325	Detecting Promotion Campaigns in Community Question Answering	Xin Li, Yiqun Liu, Min Zhang, Shaoping Ma, Xuan Zhu, Jiashen Sun	We propose a propagation algorithm to diffuse promotion intents on an "answerer-channel" bipartite graph and detect possible spamming activities.
326	VRCA: A Clustering Algorithm for Massive Amount of Texts	Ming Liu, Lei Chen, Bingquan Liu, Xiaolong Wang	Therefore, to cluster texts in large amount, this paper proposes a novel clustering algorithm, where only the features that can represent cluster are preserved in cluster’s vector.
327	Towards Domain-Specific Semantic Relatedness: A Case Study from Geography	Shilad Sen, Isaac Johnson, Rebecca Harper, Huy Mai, Samuel Horlbeck Olsen, Benjamin Mathers, Laura Souza Vonessen, Matthew Wright, Brent Hecht	This paper introduces domain-specific SR, which augments general SR by identifying, capturing, and synthesizing domain-specific relationships between concepts.
328	Interest Inference via Structure-Constrained Multi-Source Multi-Task Learning	Xuemeng Song, Liqiang Nie, Luming Zhang, Maofu Liu, Tat-Seng Chua	In this work, we propose a structure-constrained multi-source multi-task learning scheme to co-regularize the source consistency and the tree-guided task relatedness. In addition, we have released our dataset to facilitate the research communities.
329	Unsupervised Sentiment Analysis for Social Media Images	Yilin Wang, Suhang Wang, Jiliang Tang, Huan Liu, Baoxin Li	In this paper,we study the problem of understanding human sentiments from large-scale social media images,considering both visual content and contextual information,such as comments on the images, captions,etc.
330	Re-Ranking Voting-Based Answers by Discarding User Behavior Biases	Xiaochi Wei, Heyan Huang, Chin-Yew Lin, Xin Xin, Xianling Mao, Shangguang Wang	In this paper, we solve this problem by analyzing two kinds of biases; position bias and appearance bias.
331	A Unified Probabilistic Model of User Activities and Relations on Social Networking Sites	Xiaofeng Yu, Junqing Xie, Shuai Wang	In this work, we investigate the bidirectional mutual interactions (BMI) between users’ activities and user-user relationships on social networking sites.
332	Exploiting k-Degree Locality to Improve Overlapping Community Detection	Hongyi Zhang, Michael R. Lyu, Irwin King	In this paper, we propose a Locality-based Non-negative Matrix Factorization (LNMF) model to refine a preference-based model by incorporating locality into learning objective.
333	Learning Geographical Hierarchy Features for Social Image Location Prediction	Xiaoming Zhang, Xia Hu, Zhoujun Li	In this paper, we propose a geographically hierarchical bi-modal deep belief network model (GH-BDBN), which is a compositional learning architecture that integrates multi-modal deep learning model with non-parametric hierarchical prior model.
334	Catch the Black Sheep: Unified Framework for Shilling Attack Detection Based on Fraudulent Action Propagation	Yongfeng Zhang, Yunzhi Tan, Min Zhang, Yiqun Liu, Tat-Seng Chua, Shaoping Ma	In this work, we propose to conduct shilling attack detection for more informed recommendation by fraudulent action propagation on the reviews themselves, without caring about the specific underlying cheating strategy, which allows us a unified and flexible framework to detect the spam users.
335	Tackling Data Sparseness in Recommendation using Social Media based Topic Hierarchy Modeling	Xingwei Zhu, Zhao-Yan Ming, Yu Hao, Xiaoyan Zhu	This paper presents a novel method to tackle the problem of data sparseness in user ratings with rich and timely domain information from social media.
336	Artificial Intelligence in the Concertgebouw	Andreas Arzt, Harald Frostel, Thassilo Gadermaier, Martin Gasser, Maarten Grachten, Gerhard Widmer	In this paper we present a real-world application (the first of its kind) of machine listening in the context of a live concert in a world-famous concert hall – the Concertgebouw in Amsterdam.
337	Kinetic Imaginations: Exploring the Possibilities of Combining AI and Dance	Alexander Berman, Valencia James	This paper presents an interdisciplinary project which aims at cross-fertilizing dance with artificial intelligence.
338	Heroic versus Collaborative AI for the Arts	Mark d’Inverno, Jon McCormack	In this context we consider the nature of the relationship between AI and Art and introduce two opposing concepts: that of “Heroic AI”, to describe the situation where the software takes on the role of the lone creative hero and “Collaborative AI” where the system supports, challenges and provokes the creative activity of humans.
339	Computational Invention of Cadences and Chord Progressions by Conceptual Chord-Blending	Manfred Eppe, Roberto Confalonieri, Ewen MacLean, Maximos Kaliakatsos, Emilios Cambouropoulos, Marco Schorlemmer, Mihai Codescu, Kai-Uwe Kühnberger	We present a computational framework for chord invention based on a cognitive-theoretic perspective on conceptual blending.
340	Slogans Are Not Forever: Adapting Linguistic Expressions to the News	Lorenzo Gatti, Gözde özbal, Marco Guerini, Oliviero Stock, Carlo Strapparava	In this work we present a system that takes existing well-known expressions and innovates them by bringing in a novel concept coming from evolving news.
341	Pseudo-Supervised Training Improves Unsupervised Melody Segmentation	Stefan Lattner, Carlos Eduardo Cancino Chacón, Maarten Grachten	More specifically, we use information content estimates computed from a generative model of the data as a target for a feed-forward neural network that is trained to estimate the information content directly from the data.
342	Swarm Systems in the Visualization of Consumption Patterns	Catarina Maçãs, Pedro Cruz, Pedro Martins, Penousal Machado	In this paper, we apply a swarm based system as a method to create emergent visualizations of data that convey meaningful information in an inciting way, exploring the boundaries between Data Visualization and Information Aesthetics.
343	Evolving Ambiguous Images	Penousal Machado, Adriano Vinhas, João Correia, Aniko Ekárt	This work explores the creation of ambiguous images, i.e., images that may induce multistable perception, by evolutionary means.
344	The Scaffolded Sound Beehive	AnneMarie Maes	The Scaffolded Sound Beehive
345	Generating 1/f Noise Sequences as Constraint Satisfaction: The Voss Constraint	François Pachet, Pierre Roy, Alexandre Papadopoulos, Jason Sakellariou	In this paper, we formulate the generation of 1/f series as a hard constraint satisfaction problem, so that 1/f noise generation can be used as an add-on to arbitrary sequence generation problems.
346	Generating all Possible Palindromes from Ngram Corpora	Alexandre Papadopoulos, Pierre Roy, Jean-Charles Régin, François Pachet	We address the problem of generating all possible palindromes from a corpus of Ngrams.
347	Haiku Generator that Reads Blogs and Illustrates Them with Sounds and Images	Rafal Rzepka, Kenji Araki	In this paper we introduce our haiku generator, which, in contrast to other systems, is not restricted to limited classic vocabulary sets and preserves a classic style without becoming too random and abstract because it performs a semantic integrity check using the Internet.
348	Looking at Mondrian’s Victory Boogie-Woogie: What Do I Feel?	Andreza Sartori, Yan Yan, Gözde özbal, Alkim Almila Akdag Salah, Albert Ali Salah, Nicu Sebe	In this paper we explore if the same metadata could facilitate the computational analysis of artworks, and reveal what kind of emotional responses they awake.
349	Aesthetic Visual Quality Evaluation of Chinese Handwritings	Rongju Sun, Zhouhui Lian, Yingmin Tang, Jianguo Xiao	This paper attempts to solve this problem by proposing a number of aesthetic feature representations and feeding them into Artificial Neural Networks.
350	Narrative Hermeneutic Circle: Improving Character Role Identification from Natural Language Text via Feedback Loops	Josep Valls-Vargas, Jichen Zhu, Santiago Ontanon	In the task of automatically identifying characters and their narrative roles, we propose a feedback-loop-based approach where the output of later modules of the pipeline is fed back to earlier ones.
351	Learning to Rap Battle with Bilingual Recursive Neural Networks	Dekai Wu, Karteek Addanki	We describe an unconventional line of attack in our quest to teach machines how to rap battle by improvising hip hop lyrics on the fly, in which a novel recursive bilingual neural network, TRAAM, implicitly learns soft, context-dependent generalizations over the structural relationships between associated parts of challenge and response raps, while avoiding the exponential complexity costs that symbolic models would require.
352	Stroke-Based Stylization Learning and Rendering with Inverse Reinforcement Learning	Ning Xie, Tingting Zhao, Feng Tian, Xiao Hua Zhang, Masashi Sugiyama	In this paper, we develop an AI-aided art authoring (A4) system of non-photorealistic rendering that allows users to automatically generate brush stroke paintings in a specific artist’s style.
353	Online Fair Division: Analysing a Food Bank Problem	Martin Damyanov Aleksandrov, Haris Aziz, Serge Gaspers, Toby Walsh	We study an online model of fair division designed to capture features of a real world charity problem.
354	A Personalised Thermal Comfort Model Using a Bayesian Network	Frederik Auffenberg, Sebastian Stein, Alex Rogers	In this paper, we address the challenge of predicting optimal comfort temperatures of individual users of a smart heating system.
355	Aggregate Demand-Based Real-Time Pricing Mechanism for the Smart Grid: A Game-Theoretic Analysis	Sambaran Bandyopadhyay, Ramasuri Narayanam, Ramachandra Kota, Pg Dr Mohammad Iskandarbin Pg Hj Petra, Zainul Charbiwala	In this paper, we examine a model of ex-post real-time pricing mechanism that can be used by the utilities for this purpose.
356	Batch Reinforcement Learning for Smart Home Energy Management	Heider Berlink, Anna HR Costa	We propose a new energy management system (called RLbEMS) that autonomously defines a policy for selling or storing energy surplus in smart homes.
357	Reasoning about Connectivity Constraints	Christian Bessiere, Emmanuel Hebrard, George Katsirelos, Toby Walsh	To reason about connectivity, we propose a new family of global connectivity constraints.
358	Clustering Dynamic Spatio-Temporal Patterns in The Presence of Noise and Missing Data	Xi Chen, James H. Faghmous, Ankush Khandelwal, Vipin Kumar	We propose a new spatio-temporal data mining paradigm, to autonomously identify dynamic spatio-temporal clusters in the presence of noise and missing data.
359	α-min: A Compact Approximate Solver For Finite-Horizon POMDPs	Yann Dujardin, Tom Dietterich, Iadine Chades	Building on previous point-based POMDP solvers, this paper introduces a new algorithm (alpha-min) that formulates a Mixed Integer Linear Program (MILP) to calculate approximate solutions for finite-horizon POMDP problems with limited numbers of alpha-vectors.
360	When Security Games Go Green: Designing Defender Strategies to Prevent Poaching and Illegal Fishing	Fei Fang, Peter Stone, Milind Tambe	This paper (i) introduces Green Security Games (GSGs), a novel game model for green security domains with a generalized Stackelberg assumption; (ii) provides algorithms to plan effective sequential defender strategies — such planning was absent in previous work; (iii) proposes a novel approach to learn adversary models that further improves defender performance; and (iv) provides detailed experimental analysis of proposed approaches.
361	Modeling Multi-Attribute Demand for Sustainable Cloud Computing with Copulae	Maryam Ghasemi, Benjamin Lubin	Existing models have only treated a single resource type, such as CPU, or memory, at a time.
362	On the Balance of Meter Deployment Cost and NILM Accuracy	Xiaohong Hao, Bangsheng Tang, Yongcai Wang	With the notation of a clearness function, we propose solutions to the smart meter deployment problem (SMDP), that is, the problem of finding a deployment scheme with minimum number of meters while attaining a required monitoring accuracy.
363	Online Mechanisms for Charging Electric Vehicles in Settings with Varying Marginal Electricity Costs	Keiichiro Hayakawa, Enrico H. Gerding, Sebastian Stein, Takahiro Shiga	We propose new mechanisms that can be used by a demand response aggregator to flexibly shift the charging of electric vehicles (EVs) to times where cheap but intermittent renewable energy is in high supply.
364	Secure Routing in Wireless Sensor Networks via POMDPs	Athirai A. Irissappane, Jie Zhang, Frans A. Oliehoek, Partha S. Dutta	In this paper, we propose a hierarchical POMDP based approach to make routing decisions with partial/limited information about the sensor nodes, in a secure and energy-efficient manner.
365	Approximately Stable Pricing for Coordinated Purchasing of Electricity	Andrew Perrault, Craig Boutilier	We investigate their application to pricing and cost sharing in group purchasing of electricity in smart grid settings.
366	Multiple Instance Learning-Based Birdsong Classification Using Unsupervised Recording Segmentation	Jose F. Ruiz-Muñoz, Mauricio Orozco Alzate, G. Castellanos-Dominguez	Particularly, this paper is focused on the extraction of local information as units –called instances– from audio recordings.
367	Abstract Routing Models and Abstractions in the Context of Vehicle Routing	René Schönfelder, Martin Leucker	We consider two major examples, namely the time-dependent routing problem for public transportation and the energy-efficient routing problem for electric vehicles.
368	Copula Graphical Models for Wind Resource Estimation	Kalyan Veeramachaneni, Alfredo Cuesta-Infante, Una-May O’Reilly	We develop multivariate copulas for modeling multiple joint distributions of wind speeds at a wind farm site and neighboring wind source.
369	Fast Combinatorial Algorithm for Optimizing the Spread of Cascades	Xiaojian Wu, Daniel Sheldon, Shlomo Zilberstein	We propose a fast combinatorial optimization algorithm using Lagrangian relaxation and primal-dual techniques to solve the problem approximately.
370	Optimal Electric Vehicle Charging Station Placement	Yanhai Xiong, Jiarui Gan, Bo An, Chunyan Miao, Ana L. C. Bazzan	Properties of CSPL problem are analyzed and an algorithm called OCEAN is proposed to compute the optimal allocation of charging stations.
371	A Crowdfunding Model for Green Energy Investment	Ronghuo Zheng, Ying Xu, Nilanjan Chakraborty, Katia Sycara	In this paper we develop a sequential game theory model to capture the interactions among crowdfunders, the solar farm owner, and an electricity company who purchases renewable energy generated by the solar farm in a multi-period framework.
372	A MaxSAT Algorithm Using Cardinality Constraints of Bounded Size	Mario Alviano, Carmine Dodaro, Francesco Ricca	The paper introduces a new core-guided algorithm that adds cardinality constraints for each detected core, but also limits the number of literals in each constraint in order to control the number of refutations in subsequent satisfiability checks.
373	Stable Model Semantics of Abstract Dialectical Frameworks Revisited: A Logic Programming Perspective	Mario Alviano, Wolfgang Faber	This paper relates two extensively studied formalisms: abstract dialectical frameworks and logic programs with generalized atoms or similar constructs.
374	Combining Existential Rules and Description Logics	Antoine Amarilli, Michael Benedikt	This work investigates how to get the best of both worlds: having decidable existential rules on arbitrary arity relations, while allowing rich description logics, including functionality constraints, on arity-two relations. Second, we introduce an expressive set of existential rules (frontier-one rules with a certain restriction) which can be combined with powerful constraints on arity-two relations (e.g. GC2, ALCQIb) while retaining decidable query answering.
375	Bidirectional Constraints for Exchanging Data: Beyond Monotone Queries	Marcelo Arenas, Gabriel Diéguez, Jorge Pérez	In this paper, we propose to use the language of bidirectional constraints to specify schema mappings in the context of data exchange.
376	First-Order Rewritability of Temporal Ontology-Mediated Queries	Alessandro Artale, Roman Kontchakov, Alisa Kovtunova, Vladislav Ryzhikov, Frank Wolter, Michael Zakharyaschev	Aiming at ontology-based data access over temporal, in particular streaming data, we design a language of ontology-mediated queries by extending OWL 2 QL and SPARQL with temporal operators, and investigate rewritability of these queries into two-sorted first-order logic with < and PLUS over time.
377	Multi-Agent Only Knowing on Planet Kripke	Guillaume Aucher, Vaishak Belle	We propose a new account based on Moss’ characteristic formulas, formulated for the usual Kripke semantics.
378	Combining Existential Rules and Transitivity: Next Steps	Jean-François Baget, Meghyn Bienvenu, Marie-Laure Mugnier, Swan Rocher	In this paper, we address the issue of whether transitivity can be safely combined with decidable classes of existential rules.
379	Dealing with Generic Contrariness in Structured Argumentation	Pietro Baroni, Massimiliano Giacomin, Beishui Liao	With the aim of preserving the same level of generality, the paper provides a solution based on a novel notion of closure of the contrariness relation at the level of sets of formulas and an abstract representation of conflicts between sets of arguments.
380	AGM Meets Abstract Argumentation: Expansion and Revision for Dung Frameworks	Ringo Baumann, Gerhard Brewka	In this paper we combine two of the most important areas of knowledge representation, namely belief revision and (abstract) argumentation.
381	Answer Update for Rule-Based Stream Reasoning	Harald Beck, Minh Dao-Tran, Thomas Eiter	To address this problem, we present a method to efficiently update models of a rule set.
382	Epistemic Quantified Boolean Logic: Expressiveness and Completeness Results	Francesco Belardinelli, Wiebe van der Hoek	We introduce epistemic quantified boolean logic (EQBL), an extension of propositional epistemic logic with quantification over propositions.
383	Only Knowing Meets Common Knowledge	Vaishak Belle, Gerhard Lakemeyer	In this work, we lift that serious limitation to obtain a first-order language with only knowing and common knowledge, allowing us to study the interaction between these notions for the very first time.
384	ALLEGRO: Belief-Based Programming in Stochastic Dynamical Domains	Vaishak Belle, Hector Levesque	This paper presents ALLEGRO, a belief-based programming language for stochastic domains, that refashions GOLOG to allow for discrete and continuous initial uncertainty and noise.
385	Probabilistic Inference in Hybrid Domains by Weighted Model Integration	Vaishak Belle, Andrea Passerini, Guy Van den Broeck	In this paper, we introduce a strict generalization of WMC called weighted model integration that is based on annotating Boolean and arithmetic constraints, and combinations thereof.
386	Compatible-Based Conditioning in Interval-Based Possibilistic Logic	Salem Benferhat, Amélie Levray, Karim Tabia, Vladik Kreinovich	This paper focuses on the fundamental issue of conditioning in the interval-based possibilistic setting.
387	Partial Grounded Fixpoints	Bart Bogaerts, Joost Vennekens, Marc Denecker	The study of groundedness was limited to exact lattice points; in this paper, we extend it to the bilattice: for an approximator A of O, we define A-groundedness.
388	Complexity Results in Epistemic Planning	Thomas Bolander, Martin Holm Jensen, Francois Schwarzentruber	We show that moving from epistemic preconditions to propositional preconditions makes it decidable, more precisely in EXPSPACE.
389	Policies that Generalize: Solving Many Planning Problems with the Same Policy	Blai Bonet, Hector Geffner	We establish conditions under which memoryless policies and finite-state controllers that solve one partially observable non-deterministic problem (PONDP) generalize to other problems; namely, problems that have a similar structure and share the same action and observation space.
390	On the Entailment Problem for a Logic of Typicality	Richard Booth, Giovanni Casini, Thomas Andreas Meyer, Ivan José Varzinczak	In the spirit of this interpretation, we define two primary forms of entailment for PTL and discuss their advantages and disadvantages.
391	The Complexity of Subsumption in Fuzzy EL	Stefan Borgwardt, Marco Cerami, Rafael Peñaloza	We show that this does not hold for fuzzy extensions of the light-weight DL EL, which is used in many biomedical ontologies, under the Lukasiewicz semantics.
392	Temporal Query Answering in the Description Logic EL	Stefan Borgwardt, Veronika Thost	We investigate temporalized OBDA w.r.t. ontologies formulated in EL, a description logic that allows for efficient reasoning and is successfully used in practice.
393	Reasonable Highly Expressive Query Languages	Pierre Bourhis, Markus Krötzsch, Sebastian Rudolph	We introduce a new query language, guarded queries (GQ), which generalizes most known languages where query containment is decidable.
394	Logic Program Termination Analysis Using Atom Sizes	Marco Calautti, Sergio Greco, Cristian Molinaro, Irina Trubitsyna	In this paper, we propose a novel class of logic programs whose evaluation always terminates.
395	On the Undecidability of the Situation Calculus Extended with Description Logic Ontologies	Diego Calvanese, Giuseppe De Giacomo, Mikhail Soutchanski	In this paper we investigate situation calculus action theories extended with ontologies, expressed as description logics TBoxes that act as state constraints.
396	Verification of Generalized Inconsistency-Aware Knowledge and Action Bases	Diego Calvanese, Marco Montali, Ario Santoso	This work provides a twofold contribution along this line of research.
397	Probabilistic Belief Contraction Using Argumentation	Kinzang Chhogyal, Abhaya Nayak, Zhiqiang Zhuang, Abdul Sattar	We, therefore, propose a novel approach to probabilistic belief contraction using argumentation.
398	Tractable Learning for Structured Probability Spaces: A Case Study in Learning Preference Distributions	Arthur Choi, Guy Van den Broeck, Adnan Darwiche	We develop general-purpose techniques for probabilistic reasoning and learning with PSDDs, allowing one to compute the probabilities of arbitrary logical formulas and to learn PSDDs from incomplete data.
399	An Algebra of Granular Temporal Relations for Qualitative Reasoning	Quentin Cohen-Solal, Maroua Bouzid, Alexandre Niveau	In this paper, we propose a qualitative formalism for representing and reasoning about time at different scales.
400	Extension Enforcement in Abstract Argumentation as an Optimization Problem	Sylvie Coste-Marquis, Sébastien Konieczny, Jean-Guy Mailly, Pierre Marquis	We show how the enforcement problem for the operators of the family can be modeled as a pseudo-Boolean optimization problem.
401	Controlled Query Evaluation for Datalog and OWL 2 Profile Ontologies	Bernardo Cuenca Grau, Evgeny Kharlamov, Egor V. Kostylev, Dmitriy Zheleznyakov	We study confidentiality enforcement in ontologies under the Controlled Query Evaluation framework, where a policy specifies the sensitive information and a censor ensures that query answers that may compromise the policy are not returned.
402	Multilateral Negotiation in Boolean Games with Incomplete Information Using Generalized Possibilistic Logic	Sofie De Clercq, Steven Schockaert, Ann Nowé, Martine De Cock	In this paper we investigate how agents in Boolean games can reach an efficient and fair outcome through a simple negotiation protocol.
403	Fixed-Parameter Tractable Reductions to SAT for Planning	Ronald de Haan, Martin Kronegger, Andreas Pfandler	In this paper, we use the framework of parameterized complexity theory to obtain a more fine-grained complexity analysis of natural planning problems beyond NP.
404	The Logic of Qualitative Probability	James Delgrande, Bryan Renne	In this paper we present a theory of qualitative probability.
405	On the Aggregation of Argumentation Frameworks	Jérôme Delobelle, Sébastien Konieczny, Srdjan Vesic	In this work we study the existing operators and new ones that we propose in light of the proposed properties, highlighting the fact that existing operators do not satisfy a lot of these properties.
406	Combining Existential Rules with the Power of CP-Theories	Tommaso Di Noia, Thomas Lukasiewicz, Maria Vanina Martinez, Gerardo I. Simari, Oana Tifrea-Marciuska	In this paper, we explore how ontological knowledge expressed via existential rules can be combined with CP-theories to (i) represent qualitative preferences along with domain knowledge, and (ii) perform preference-based answering of conjunctive queries (CQs).
407	An Extension-Based Approach to Belief Revision in Abstract Argumentation	Martin Diller, Adrian Haret, Thomas Linsbichler, Stefan Rümmele, Stefan Woltran	In this work, we present a generic solution to this problem which applies to many prominent I-maximal argumentation semantics.
408	The Cube of Opposition: A Structure Underlying Many Knowledge Representation Formalisms	Didier Dubois, Henri Prade, Agnès Rico	After restating these results in a unified perspective, the paper proposes a graded extension of the cube and shows that several qualitative, as well as quantitative formalisms, such as Sugeno integrals used in multiple criteria aggregation and qualitative decision theory, or yet belief functions and Choquet integrals, are amenable to transformations that form graded cubes of opposition.
409	Modular Systems with Preferences	Alireza Ensan, Eugenia Ternovska	We propose a versatile framework for combining knowledge bases in modular systems with preferences.
410	A Logic for Reasoning about Justified Uncertain Beliefs	Tuan-Fang Fan, Churn-Jung Liau	The objective of this paper is to extend the graded modal logics with explicit justifications.
411	On the Progression of Knowledge and Belief for Nondeterministic Actions in the Situation Calculus	Liangda Fang, Yongmei Liu, Ximing Wen	In this paper, based on their framework, we investigate progression of both belief and knowledge in the single-agent propositional case.
412	Epistemic Equilibrium Logic	Luis Fariñas del Cerro, Andreas Herzig, Ezgi Iraz Su	We add epistemic modal operators to the language of here-and-there logic and define epistemic here-and-there models.
413	The Combined Approach to Query Answering Beyond the OWL 2 Profiles	Cristina Feier, David Carral, Giorgio Stefanoni, Bernardo Cuenca Grau, Ian Horrocks	Our goal is to make combined approaches applicable to a wider range of ontologies.
414	Computing Social Behaviours Using Agent Models	Paolo Felli, Tim Miller, Christian Muise, Adrian R. Pearce, Liz Sonenberg	We use a hierarchy of agent models to discriminate which behaviours of others are plausible, and decide which behaviour for ourselves is socially acceptable, i.e. conforms to the social context.
415	On the Computational Complexity of Naive-Based Semantics for Abstract Dialectical Frameworks	Sarah Alice Gaggl, Sebastian Rudolph, Hannes Strass	In this paper, we perform an exhaustive analysis of the computational complexity of naive-based semantics.
416	Polynomial Rewritings for Linear Existential Rules	Georg Gottlob, Marco Manna, Andreas Pieris	In this work, we close two open fundamental questions related to query rewriting.
417	Beyond SPARQL under OWL 2 QL Entailment Regime: Rules to the Rescue	Georg Gottlob, Andreas Pieris	In this work, we focus on OWL 2 QL and we propose TriQ-Lite 1.0, a tractable rule-based formalism that supports the above functionalities, and thus it can be used for querying RDF data.
418	Group Decision Making via Weighted Propositional Logic: Complexity and Islands of Tractability	Gianluigi Greco, Jerome Lang	We seek solutions maximizing utilitarian social welfare as well as fair solutions maximizing the utility of the least happy agent.
419	Lightweight Temporal Description Logics with Rigid Roles and Restricted TBoxes	Víctor Gutiérrez-Basulto, Jean Christoph Jung, Thomas Schneider	As our main contribution, we identify several TDLs of elementary complexity, obtained by combining EL with CTL fragments that allow only restricted sets of temporal operators.
420	A Modification of the Halpern-Pearl Definition of Causality	Joseph Halpern	A Modification of the Halpern-Pearl Definition of Causality
421	Efficient Query Rewriting in the Description Logic EL and Beyond	Peter Hansen, Carsten Lutz, İnanç Seylan, Frank Wolter	We propose a new type of algorithm for computing first-order (FO) rewritings of concept queries under ELHdr-TBoxes.
422	Merging in the Horn Fragment	Adrian Haret, Stefan Rümmele, Stefan Woltran	In this paper, we provide a novel representation theorem for Horn merging by strengthening the standard merging postulates.
423	Schema.org as a Description Logic	Andre Hernich, Carsten Lutz, Ana Ozaki, Frank Wolter	We formalize the language of Schema.org as a Description Logic (DL) and study the complexity of querying data using (unions of) conjunctive queries in the presence of ontologies formulated in this DL (from several perspectives).
424	Modelling the Persuadee in Asymmetric Argumentation Dialogues for Persuasion	Anthony Hunter	In this paper, we consider asymmetric dialogues where only the system presents arguments, and the system maintains a model of the user to determine the best choice of arguments to present (including counterarguments to key arguments believed to be held by the user).
425	Trust-Sensitive Belief Revision	Aaron Hunter, Richard Booth	In this paper, we define trust as a pre-processing step before revision.
426	Simplifying A Logic Program Using Its Consequences	Jianmin Ji, Hai Wan, Ziwei Huo, Zhenfeng Yuan	In this paper, we extend the notion of well-founded models to consequences for simplifying disjunctive logic programs (DLPs) in a general manner. Specifically, we provide two main notions, strong reliable set and weak reliable set, and show that a DLP is strongly equivalent to the simplified program if and only if the consequence is a strong reliable set, and they have the same answer sets if and only if the consequence is a weak reliable set.
427	On Forgetting Postulates in Answer Set Programming	Jianmin Ji, Jia-Huai You, Yisong Wang	In this paper, we are interested in the question onthe largest set Δ of pairs (Π, V), where Π is a logic program and V is a set of atoms, such that a forgetting operator exists that satisfies all the desirable properties for each (Π, V) in Δ.
428	Efficient Semantic Features for Automated Reasoning over Large Theories	Cezary Kaliszyk, Josef Urban, Jiri Vyskocil	In this work we (i) propose novel semantic features characterizing the statements in such large semantic knowledge bases, (ii) propose and carry out their efficient implementation using deductive-AI data-structures such as substitution trees and discrimination nets, and (iii) show that they significantly improve the strength of existing knowledge selection methods and automated reasoning methods over the large formal knowledge bases.
429	Computing Horn Rewritings of Description Logics Ontologies	Mark Kaminski, Bernardo Cuenca Grau	We study the problem of rewriting an ontology O1 expressed in a DL L1 into an ontology O2 in a Horn DL L2 such that O1 and O2 are equisatisfiable when extended with an arbitrary dataset.
430	Efficient Paraconsistent Reasoning with Ontologies and Rules	Tobias Kaminski, Matthias Knorr, João Leite	In this paper, we address the problem of efficiently obtaining meaningful conclusions from (possibly inconsistent) hybrid KBs.
431	Query Rewriting for Existential Rules with Compiled Preorder	Melanie Konig, Michel Leclere, Marie-Laure Mugnier	We propose a rewriting technique, which consists in compiling these rules into a preorder on atoms and embedding this preorder into the rewriting process.
432	Automatic Verification of Partial Correctness of Golog Programs	Naiqi Li, Yongmei Liu	In this paper we propose a sound but incomplete method for automatic verification of partial correctness of Golog programs.
433	Ontology-Mediated Queries with Closed Predicates	Carsten Lutz, Inanc Seylan, Frank Wolter	In the context of ontology-based data access with description logics (DLs), we study ontology-mediated queries in which selected predicates can be closed (OMQCs).
434	Combining Rewriting and Incremental Materialisation Maintenance for Datalog Programs with Equality	Boris Motik, Yavor Nenov, Robert Piro, Ian Horrocks	In this paper we present the first such combination, and we show empirically that it can speed up updates by several orders of magnitude compared to using either rewriting or incremental maintenance in isolation.
435	Kernel Contraction and Base Dependence: Redundancy in the Base Resulting in Different Types of Dependence	Mehrdad Oveisi, James P. Delgrande, Fred Popowich, Francis Jeffry Pelletier	One study offers a dependence relation parallel to AGM contraction for belief sets.
436	A Top-Down Compiler for Sentential Decision Diagrams	Umut Oztok, Adnan Darwiche	In this work, we present a top-down CNF to SDD compiler that is based on techniques from the SAT literature.
437	On the Parameterized Complexity of Belief Revision	Andreas Pfandler, Stefan Rümmele, Johannes Peter Wallner, Stefan Woltran	This is somewhat surprising, since by its very nature of involving a knowledge base and a revision formula, this problem provides a perfect playground for investigating novel parameters.
438	Probabilistic Reasoning with Inconsistent Beliefs Using Inconsistency Measures	Nico Potyka, Matthias Thimm	We generalize this problem by omitting the consistency assumption and, thus, provide a general framework for probabilistic reasoning under inconsistency.
439	Did You Know? — Mining Interesting Trivia for Entities from Wikipedia	Abhay Prakash, Manoj Kumar Chinnakotla, Dhaval Patel, Puneet Garg	In this paper, we propose a novel approach for mining entity trivia from their Wikipedia pages.
440	Realizability of Three-Valued Semantics for Abstract Dialectical Frameworks	Jörg Pührer	We investigate fundamental properties of three-valued semantics for abstract dialectical frameworks (ADFs).
441	Execution Monitoring as Meta-Games for General Game-Playing Robots	David Rajaratnam, Michael Thielscher	We develop a formal framework for execution monitoring by which an action theory that provides an axiomatic description of a game is automatically embedded in a meta-game for a robotic player — called the arbiter — whose role is to monitor and correct failed actions.
442	Membership Constraints in Formal Concept Analysis	Sebastian Rudolph, Christian Sacarea, Diana Troanca	We present a generic answer set programming (ASP) encoding of the membership constraint satisfaction problem, which allows for deploying available highly optimized ASP tools for its solution.
443	Characterization of the Expressivity of Existential Rule Queries	Sebastian Rudolph, Michaël Thomazo	We consider them here as a querying formalism, extending classical Datalog, the language of deductive databases.
444	Towards Fully Observable Non-Deterministic Planning as Assumption-based Automatic Synthesis	Sebastian Sardina, Nicolas D’Ippolito	Whereas previous work on non-deterministic planning has focused on characterizing (and computing) "loopy" but "closed" plans, we look here at the kind of environments that these plans are to be executed in.
445	Qualitative Reasoning about Directions in Semantic Spaces	Steven Schockaert, Jae Hee Lee	We introduce a framework for qualitative reasoning about directions in high-dimensional spaces, called EER, where our main motivation is to develop a form of commonsense reasoning about semantic spaces.
446	Belief Revision and Progression of Knowledge Bases in the Epistemic Situation Calculus	Christoph Schwering, Gerhard Lakemeyer, Maurice Pagnucco	In this paper we propose a novel framework for computing progression in the epistemic situation calculus.
447	MergeXplain: Fast Computation of Multiple Conflicts for Diagnosis	Kostyantyn Shchekotykhin, Dietmar Jannach, Thomas Schmitz	In this paper we propose MergeXPlain, a non-intrusive conflict detection algorithm which implements a divide-and-conquer strategy to decompose a problem into a set of smaller independent subproblems.
448	Efficiently Characterizing Non-Redundant Constraints in Large Real World Qualitative Spatial Networks	Michael Sioutis, Sanjiang Li, Jean-Francois Condotta	We focus on efficiently characterizing non-redundant constraints in large real world RCC8 networks and obtaining their prime networks.
449	Characterizability in Belief Revision	György Turán, Jon Yaggie	A formal framework is given for the postulate characterizability of a class of belief revision operators, obtained from a class of partial preorders using minimization.
450	Efficiently Finding Conditional Instruments for Causal Inference	Benito van der Zander, Johannes Textor, Maciej Liskiewicz	A generalized IV method has been proposed that only requires exogeneity conditional on a set of covariates.
451	AGM Revision of Beliefs about Action and Time	Marc van Zee, Dragan Doder, Mehdi Dastani, Leendert van der Torre	In this paper we develop a logic for revision of temporal belief bases, containing expressions about temporal propositions (tomorrow it will rain), possibility (it may rain tomorrow), actions (the robot enters the room) and pre- and post-conditions of these actions.
452	A Complete Epistemic Planner without the Epistemic Closed World Assumption	Hai Wan, Rui Yang, Liangda Fang, Yongmei Liu, Huada Xu	In this paper, we propose a complete single-agent epistemic planner without the ECWA.
453	Query Understanding through Knowledge-Based Conceptualization	Zhongyuan Wang, Kejun Zhao, Haixun Wang, Xiaofeng Meng, Ji-Rong Wen	In this paper, we first mine a variety of relations among terms from a large web corpus and map them to related concepts using a probabilistic knowledge base.
454	Computation and Complexity of Preference Inference Based on Hierarchical Models	Nic Wilson, Anne-Marie George, Barry O’Sullivan	In this paper we consider a situation in which alternatives have an associated vector of costs, each component corresponding to a different criterion, and are compared using a kind of lexicographic order, similar to the way alternatives are compared in a Hierarchical Constraint Logic Programming model.
455	Verification of Knowledge-Based Programs over Description Logic Actions	Benjamin Zarrieß, Jens Claßen	In this paper we consider a setting where an agent is equipped with a Description Logic (DL) knowledge base providing general domain knowledge and an incomplete description of the initial situation.
456	Characterizing Causal Action Theories and Their Implementations in Answer Set Programming: Action Languages B, C, and Beyond	Haodi Zhang, Fangzhen Lin	In particular, we propose to consider what we call permissible translations from these causal action theories to logic programs.
457	First-Order Disjunctive Logic Programming vs Normal Logic Programming	Yi Zhou	In this paper, we study the expressive power of first-order disjunctive logic programming (DLP) and normal logic programming (NLP) under the stable model semantics.
458	Extending AGM Contraction to Arbitrary Logics	Zhiqiang Zhuang, Zhe Wang, Kewen Wang, James P Delgrande	In this paper, we present a new entrenchment-based contraction which does not rely on any logical connectives except conjunction.
459	Learning Regular Languages via Alternating Automata	Dana Angluin, Sarah Eisenstat, Dana Fisman	In this paper we introduce UL* — a learning algorithm for universal automata (the dual of non-deterministic automata); and AL* — a learning algorithm for alternating automata (which generalize both universal and non-deterministic automata).
460	Maximum Entropy Semi-Supervised Inverse Reinforcement Learning	Julien Audiffren, Michal Valko, Alessandro Lazaric, Mohammad Ghavamzadeh	We introduce MESSI,a novel algorithm that combines MaxEnt-IRL with principles coming from semisupervised learning.
461	A Graph Kernel Based on the Jensen-Shannon Representation Alignment	Lu Bai, Zhihong Zhang, Chaoyan Wang, Xiao Bai, Edwin Hancock	In this paper, we develop a novel graph kernel by aligning the Jensen-Shannon (JS) representations of vertices.
462	An Expectation-Maximization Algorithm to Compute a Stochastic Factorization From Data	Andre M. S. Barreto, Rafael L. Beirigo, Joelle Pineau, Doina Precup	In this paper we show that the stochastic-factorization trick can also provide benefits in terms of the number of samples needed to estimate a transition matrix.
463	Count-Based Frequency Estimation with Bounded Memory	Marc G. Bellemare	In this paper we propose three novel ideas for approximating count-based estimators using bounded memory.
464	Autonomous Cross-Domain Knowledge Transfer in Lifelong Policy Gradient Reinforcement Learning	Haitham Bou Ammar, Eric Eaton, Jose Marcio Luna, Paul Ruvolo	In this paper, we develop the first cross-domain lifelong RL framework.
465	Reinforcement Learning from Demonstration through Shaping	Tim Brys, Anna Harutyunyan, Halit Bener Suay, Sonia Chernova, Matthew E. Taylor, Ann Nowé	In this paper, we investigate the intersection of these two approaches, leveraging the theoretical guarantees provided by reinforcement learning, and using expert demonstrations to speed up this learning by biasing exploration through a process called reward shaping.
466	Compressed Spectral Regression for Efficient Nonlinear Dimensionality Reduction	Deng Cai	In this paper, we propose a novel nonlinear dimensionality reduction algorithm, called Compressed Spectral Regression, with O(n) computational complexity.
467	Policy Shaping with Human Teachers	Thomas Cederborg, Ishaan Grover, Charles L Isbell, Andrea L Thomaz	In this work we evaluate the performance of a policy shaping algorithm using 26 human teachers.
468	A Space Alignment Method for Cold-Start TV Show Recommendations	Shiyu Chang, Jiayu Zhou, Pirooz Chubak, Junling Hu, Thomas Huang	In this paper, we introduce a novel hybrid recommendation algorithm incorporating both collaborative user-item relationship as well as item content features.
469	Direct Policy Iteration with Demonstrations	Jessica Chemali, Alessandro Lazaric	We consider the problem of learning the optimal policy of an unknown Markov decision process (MDP) when expert demonstrations are available along with interaction samples.
470	Model Metric Co-Learning for Time Series Classification	Huanhuan Chen, Fengzhen Tang, Peter Tino, Anthony G. Cohn, Xin Yao	We introduce a novel hybrid approach spanning the two extremes.
471	Training-Efficient Feature Map for Shift-Invariant Kernels	Xixian Chen, Haiqin Yang, Irwin King, Michael R. Lyu	We propose a novel feature map method by extending Random Kitchen Sinks through fast data-dependent subspace embedding to generate the desired features.
472	Mirror Representation for Modeling View-Specific Transform in Person Re-Identification	Ying-Cong Chen, Wei-Shi Zheng, Jianhuang Lai	In this work, we propose an effective, low cost and easy-to-apply schema called the Mirror Representation, which embeds the view-specific feature transformation and enables alignment of the feature distributions across disjoint views for the same person.
473	Efficient Generalized Conditional Gradient with Gradient Sliding for Composite Optimization	Yiu-ming Cheung, Jian Lou	In this paper, we therefore propose a novel algorithm that requires optimal graduate evaluations as proximal gradient.
474	Robust Learning for Repeated Stochastic Games via Meta-Gaming	Jacob W. Crandall	In this paper, we introduce a method to reduce the strategy space of two-player general-sum RSGs to a handful of expert strategies.
475	Learning Efficient Logical Robot Strategies Involving Composable Objects	Andrew Cropper, Stephen H. Muggleton	We introduce a new MIL implementation, MetagolO, and prove its convergence, with increasing numbers of randomly chosen examples to optimal strategies of this kind.
476	Optimal Bayesian Hashing for Efficient Face Recognition	Qi Dai, Jianguo Li, Jun Wang, Yurong Chen, Yu-Gang Jiang	In this paper, we propose a novel method called Bayesian Hashing to learn an optimal Hamming embedding of high-dimensional features, with a focus on the challenging application of face recognition.
477	Multi-View Matrix Decomposition: A New Scheme for Exploring Discriminative Information	Cheng Deng, Zongting Lv, Wei Liu, Junzhou Huang, Dacheng Tao, Xinbo Gao	In this paper, we propose a new multi-view, low-rank, and sparse matrix decomposition scheme to seamlessly integrate diverse yet complementary information stemming from multiple views.
478	Intersecting Manifolds: Detection, Segmentation, and Labeling	Shay Deutsch, Gerard Guy Medioni	In this paper we propose a novel procedure for clustering intersecting multi-manifolds and delineating junctions in high dimensional spaces.
479	Deep Low-Rank Coding for Transfer Learning	Zhengming Ding, Ming Shao, Yun Fu	In this paper, we develop a novel approach, called Deep Low-Rank Coding (DLRC), for transfer learning.
480	Speeding Up Automatic Hyperparameter Optimization of Deep Neural Networks by Extrapolation of Learning Curves	Tobias Domhan, Jost Tobias Springenberg, Frank Hutter	Automated hyperparameter optimization methods have recently been shown to yield settings competitive with those found by human experts, but their widespread adoption is hampered by the fact that they require more computational resources than human experts.
481	Topic Modeling with Document Relative Similarities	Jianguang Du, Jing Jiang, Dandan Song, Lejian Liao	In this paper, we propose a general model that links LDA with constraints derived from document relative similarities.
482	Robust Multiple Kernel K-means Using L21-Norm	Liang Du, Peng Zhou, Lei Shi, Hanmo Wang, Mingyu Fan, Wenjian Wang, Yi-Dong Shen	Robust Multiple Kernel K-means Using L21-Norm
483	Crowdsourced Semantic Matching of Multi-Label Annotations	Lei Duan, Satoshi Oyama, Masahito Kurihara, Haruhiko Sato	Given that (1) different taxonomies used in the same domain are generally founded on the same latent semantic space, where each possible label set in a taxonomy denotes a single semantic concept, and that (2) crowdsourcing is beneficial in identifying relationships between semantic concepts and instances at low cost, we proposed a novel probabilistic cascaded method for establishing a semantic matching function in a crowdsourcing setting that maps label sets in one (source) taxonomy to label sets in another (target) taxonomy in terms of the semantic distances between them.
484	Random Feature Mapping with Signed Circulant Matrix Projection	Chang Feng, Qinghua Hu, Shizhong Liao	In this paper, we propose a novel random feature mapping method that uses a signed Circulant Random Matrix (CRM) instead of an unstructured random matrix to project input data.
485	Quiet: Faster Belief Propagation for Images and Related Applications	Yasuhiro Fujiwara, Dennis Shasha	This paper presents an efficient approach to belief propagation.
486	Potential Based Reward Shaping for Hierarchical Reinforcement Learning	Yang Gao, Francesca Toni	In this paper, we investigate the integration of PBRS and HRL, and propose a new algorithm: PBRS-MAXQ-0.
487	Pre-release Prediction of Crowd Opinion on Movies by Label Distribution Learning	Xin Geng, Peng Hou	In order to solve this problem, a Label Distribution Support Vector Regressor (LDSVR) is proposed in this paper.
488	Multitask Coactive Learning	Robby Goetschalckx, Alan Fern, Prasad Tadepalli	In this paper we investigate the use of coactive learning in a multitask setting.
489	Multi-Label Structure Learning with Ising Model Selection	Andre R. Goncalves, Fernando J. Von Zuben, Arindam Banerjee	Built on recent advances in structure learning in Ising Markov Random Fields (I-MRF), we propose a multi-label classification algorithm that explicitly estimate and incorporate label dependence into the classifiers learning process by means of a sparse convex multi-task learning formulation.Extensive experiments considering several existing multi-label algorithms indicate that the proposed method, while conceptually simple, outperforms the contenders in several datasets and performance metrics.
490	Bi-Parameter Space Partition for Cost-Sensitive SVM	Bin Gu, Victor S. Sheng, Shuo Li	To overcome this challenge, we make three main steps in this paper.
491	Online Robust Low Rank Matrix Recovery	Xiaojie Guo	This paper proposes a novel online robust low rank matrix recovery method to address these difficulties.
492	Robust Subspace Segmentation by Simultaneously Learning Data Representations and Their Affinity Matrix	Xiaojie Guo	In this paper, we propose a novel method to simultaneously learn the representations of data and the affinity matrix of representation in a unified optimization framework.
493	Active Imitation Learning of Hierarchical Policies	Mandana Hamidi, Prasad Tadepalli, Robby Goetschalckx, Alan Fern	In this paper, we study the problem of imitation learning of hierarchical policies from demonstrations.
494	Identification of Time-Dependent Causal Model: A Gaussian Process Treatment	Biwei Huang, Kun Zhang, Bernhard Schölkopf	This paper aims to identify the time-dependent causal relations from observational data.
495	A New Simplex Sparse Learning Model to Measure Data Similarity for Clustering	Jin Huang, Feiping Nie, Heng Huang	In this paper, we propose that the affinity between pairs of samples could be computed using sparse representation with proper constraints.
496	Scalable Gaussian Process Regression Using Deep Neural Networks	Wenbing Huang, Deli Zhao, Fuchun Sun, Huaping Liu, Edward Chang	We propose a scalable Gaussian process model for regression by applying a deep neural network as the feature-mapping function.
497	Training-Time Optimization of a Budgeted Booster	Yi Huang, Brian Powers, Lev Reyzin	We consider the problem of feature-efficient prediction – a setting where features have costs and the learner is limited by a budget constraint on the total cost of the features it can examine in test time.
498	Robust Dictionary Learning with Capped l1-Norm	Wenhao Jiang, Feiping Nie, Heng Huang	In this paper, aiming at learning dictionaries resistant to outliers, we proposed capped l1-norm based dictionary learning and an efficient iterative re-weighted algorithm to solve the problem.
499	Fast Cross-Validation for Incremental Learning	Pooria Joulani, Andras Gyorgy, Csaba Szepesvari	In this paper, we propose a new approach to reduce the computational burden of CV-based performance estimation.
500	Bayesian Active Learning for Posterior Estimation	Kirthevasan Kandasamy, Jeff Schneider, Barnabas Poczos	We propose two myopic query strategies to choose where to evaluate the likelihood and implement them using Gaussian processes.
501	Collaborative Place Models	Berk Kapicioglu, David S. Rosenberg, Robert E. Schapire, Tony Jebara	As a remedy, we describe a Bayesian probabilistic graphical model, called Collaborative Place Model (CPM), which infers similarities across users to construct complete and time-dependent profiles of users’ whereabouts from unsupervised location data.
502	Symbol Acquisition for Probabilistic High-Level Planning	George Konidaris, Leslie Kaelbling, Tomas Lozano-Perez	We introduce a framework that enables an agent to autonomously learn its own symbolic representation of a low-level, continuous environment.
503	Data Sparseness in Linear SVM	Xiang Li, Huaimin Wang, Bin Gu, Charles X. Ling	To study this problem in a systematic manner, we propose a novel approach to generate large and sparse data from real-world datasets, using statistical inference and the data sampling process in the PAC framework.
504	Multi-Label Classification with Feature-Aware Non-Linear Label Space Transformation	Xin Li, Yuhong Guo	We conduct experiments on a number of multi-label classification datasets.
505	Multi-Task Model and Feature Joint Learning	Ya Li, Xinmei Tian, Tongliang Liu, Dacheng Tao	In this paper, we propose a novel multi-task learning method to jointly learn shared parameters and shared feature representation.
506	Word Embedding Revisited: A New Representation Learning and Explicit Matrix Factorization Perspective	Yitan Li, Linli Xu, Fei Tian, Liang Jiang, Xiaowei Zhong, Enhong Chen	In this paper, we provide a new perspective for further understanding SGNS.
507	Mixed Error Coding for Face Recognition with Mixed Occlusions	Ronghua Liang, Xiao-Xin Li	By combining the two various errors with the occlusion support, we present an extended error coding model, dubbed Mixed Error Coding (MEC).
508	Density Corrected Sparse Recovery when R.I.P. Condition Is Broken	Ming Lin, Zhengzhong Lan, Alexander G. Hauptmann	In this paper, we study the sparse recovery problem in which the feature matrix is strictly non-R.I.P. We prove that when features exhibit cluster structures, which often happens in real applications, we are able to recover the sparse vector consistently.
509	Regularizing Flat Latent Variables with Hierarchical Structures	Rongcheng Lin, Huayu Li, Xiaojun Quan, Richang Hong, Zhiang Wu, Yong Ge	In this paper, we propose a stratified topic model(STM).
510	Robust Kernel Dictionary Learning Using a Whole Sequence Convergent Algorithm	Huaping Liu, Jie Qin, Hong Cheng, Fuchun Sun	In this paper, we propose a new optimization model to learn the robust kernel dictionary while isolating outliers in the training samples.
511	Multi-Task Multi-Dimensional Hawkes Processes for Modeling Event Sequences	Dixin Luo, Hongteng Xu, Yi Zhen, Xia Ning, Hongyuan Zha, Xiaokang Yang, Wenjun Zhang	We propose a Multi-task Multi-dimensional Hawkes Process (MMHP) for modeling event sequences where there exist multiple triggering patterns within sequences and structures across sequences.MMHP is able to model the dynamics of multiple sequences jointly by imposing structural constraints and thus systematically uncover clustering structure among sequences.We propose an effective and robust optimization algorithm to learn MMHP models, which takes advantage of alternating direction method of multipliers (ADMM), majorization minimization and Euler-Lagrange equations.Our experimental results demonstrate that MMHP performs well on both synthetic and real data.
512	Between Imitation and Intention Learning	James MacGlashan, Michael L. Littman	In this work, we introduce receding horizon inverse reinforcement learning, in which the planning horizon induces a continuum between these two learning paradigms.
513	Optimizing Locally Linear Classifiers with Supervised Anchor Point Learning	Xue Mao, Zhouyu Fu, Ou Wu, Weiming Hu	In this paper, we present a novel fully supervised approach for anchor point learning.
514	Using A* for Inference in Probabilistic Classifier Chains	Deiner Mena, Elena Montañés, José Ramón Quevedo, Juan José del Coz	Lately, several approaches have been proposed to overcome this issue, including beam search and an epsilon-Approximate algorithm based on uniform-cost search.
515	Introspective Forecasting	Loizos Michael	We show how learning can naturally resolve this conundrum.
516	EntScene: Nonparametric Bayesian Temporal Segmentation of Videos Aimed at Entity-Driven Scene Detection	Adway Mitra, Chiranjib Bhattacharyya, Soma Biswas	In this paper, we study Bayesian techniques for entity discovery and temporal segmentation of videos.
517	Image Feature Learning for Cold Start Problem in Display Advertising	Kaixiang Mo, Bo Liu, Lei Xiao, Yong Li, Jie Jiang	In order to tackle the cold start problem in image display ads, we propose a new feature learning architecture to learn the most discriminative image features directly from raw pixels and user feedback in the target task.
518	Inverse Reinforcement Learning in Relational Domains	Thibaut Munzer, Bilal Piot, Matthieu Geist, Olivier Pietquin, Manuel Lopes	In this work, we introduce the first approach to the Inverse Reinforcement Learning (IRL) problem in relational domains.
519	On the Runtime of Randomized Local Search and Simple Evolutionary Algorithms for Dynamic Makespan Scheduling	Frank Neumann, Carsten Witt	We present the first computational complexity analysis of evolutionary algorithms for a dynamic variant of a classical combinatorial optimization problem, namely makespan scheduling.
520	Weakly Supervised Matrix Factorization for Noisily Tagged Image Parsing	Yulei Niu, Zhiwu Lu, Songfang Huang, Peng Han, Ji-Rong Wen	In this paper, we propose a Weakly Supervised Matrix Factorization (WSMF) approach to the problem of image parsing with noisy tags, i.e., segmenting noisily tagged images and then classifying the regions only with image-level labels.
521	Graph Invariant Kernels	Francesco Orsini, Paolo Frasconi, Luc De Raedt	We introduce a novel kernel that upgrades the Weisfeiler-Lehman and other graph kernels to effectively exploit high-dimensional and continuous vertex attributes.
522	EigenGP: Gaussian Process Models with Adaptive Eigenfunctions	Hao Peng, Yuan Qi	In this paper, we propose a new Bayesian approach, EigenGP, that learns both basis dictionary elements — eigenfunctions of a GP prior — and prior precisions in a sparse finite model.
523	Scalable Probabilistic Tensor Factorization for Binary and Count Data	Piyush Rai, Changwei Hu, Matthew Harding, Lawrence Carin	We develop a scalable probabilistic tensor factorization framework that enables us to perform efficient factorization of massive binary and count tensor data.
524	Nonparametric Independence Testing for Small Sample Sizes	Aaditya Ramdas, Leila Wehbe	Our main contribution is strong empirical evidence that by employing shrunk operators when the sample size is small, one can attain an improvement in power at low false positive rates.
525	Data Compression for Learning MRF Parameters	Khaled S. Refaat, Adnan Darwiche	We propose a technique for decomposing and compressing the dataset in the parameter learning problem in Markov random fields.
526	Extended Discriminative Random Walk: A Hypergraph Approach to Multi-View Multi-Relational Transductive Learning	Sai Nageswar Satchidanand, Harini Ananthapadmanaban, Balaraman Ravindran	In this work we model multi-way relations as hypergraphs and extend the discriminative random walk (DRW) framework, originally proposed for transductive inference on single graphs, to the case of multiple hypergraphs.
527	Deep Linear Coding for Fast Graph Clustering	Ming Shao, Sheng Li, Zhengming Ding, Yun Fu	In this paper, we propose a deep structure with a linear coder as the building block for fast graph clustering, called Deep Linear Coding (DLC).
528	Semi-Orthogonal Multilinear PCA with Relaxed Start	Qiquan Shi, Haiping Lu	This paper tackles this problem by proposing a novel Semi-Orthogonal Multilinear PCA (SO-MPCA) approach.
529	A Geometric Theory of Feature Selection and Distance-Based Measures	Kilho Shin, Adrian Pino Angulo	In this paper, we show a way to map arbitrary feature sets of datasets into a common metric space, which is indexed by a real number p with 1 ≤ p ≤ ∞.
530	Open Domain Short Text Conceptualization: A Generative + Descriptive Modeling Approach	Yangqiu Song, Shusen Wang, Haixun Wang	In this paper, we unify the existing conceptualization methods from a Bayesian perspective, and discuss the three modeling approaches: descriptive, generative, and discriminative models.
531	Equivalence Results between Feedforward and Recurrent Neural Networks for Sequences	Alessandro Sperduti	In the context of sequence processing, we study the relationship between single-layer feedforward neural networks,that have simultaneous access to all items composing a sequence, and single-layer recurrent neural networks which access information one step at a time.We treat both linear and nonlinear networks, describing a constructive procedure, based on linear autoencoders for sequences, that given a feedforward neural network shows how to define a recurrent neural network that implements the same function in time.
532	Polytree-Augmented Classifier Chains for Multi-Label Classification	Lu Sun, Mineichi Kudo	In this paper, we propose a novel polytree-augmented classifier chains method to remedy these problems.
533	Sketch the Storyline with CHARCOAL: A Non-Parametric Approach	Siliang Tang, Fei Wu, Si Li, Weiming Lu, Zhongfei Zhang, Yueting Zhuang	In this paper, we proposed a hddCRP (hybird distant-dependent ChineseRestaurant Process) based HierARChical tOpic model for news Article cLustering, abbreviated as CHARCOAL.
534	Convergence of Common Proximal Methods for L1-Regularized Least Squares	Shaozhe Tao, Daniel Boley, Shuzhong Zhang	We compare the convergence behavior of ADMM (alternating direction method of multipliers), [F]ISTA ([fast] iterative shrinkage and thresholding algorithm) and CD (coordinate descent) methods on the model L1-regularized least squares problem (aka LASSO).
535	Portable Option Discovery for Automated Learning Transfer in Object-Oriented Markov Decision Processes	Nicholay Topin, Nicholas Haltmeyer, Shawn Squire, John Winder, Marie desJardins, James MacGlashan	We introduce a novel framework for option discovery and learning transfer in complex domains that are represented as object-oriented Markov decision processes (OO-MDPs) [Diuk et al., 2008].
536	Online Learning of k-CNF Boolean Functions	Joel Veness, Marcus Hutter, Laurent Orseau, Marc Bellemare	This paper revisits the problem of learning a k-CNF Boolean function from examples, for fixed k, in the context of online learning under the logarithmic loss.
537	Feature Selection from Microarray Data via an Ordered Search with Projected Margin	Saulo Moraes Villla, Saul de Castro Leite, Raul Fonseca Neto	An important theoretical contribution of this paper is the development of the projected margin concept.
538	Constrained Information-Theoretic Tripartite Graph Clustering to Identify Semantically Similar Relations	Chenguang Wang, Yangqiu Song, Dan Roth, Chi Wang, Jiawei Han, Heng Ji, Ming Zhang	This paper formulates relation clustering as a constrained tripartite graph clustering problem, presents an efficient clustering algorithm and exhibits the advantage of the constrained framework.
539	Semantic Topic Multimodal Hashing for Cross-Media Retrieval	Di Wang, Xinbo Gao, Xiumei Wang, Lihuo He	Semantic Topic Multimodal Hashing for Cross-Media Retrieval
540	An Efficient Classifier Based on Hierarchical Mixing Linear Support Vector Machines	Di Wang, Xiaoqin Zhang, Mingyu Fan, Xiuzi Ye	To this end, we propose a novel classifier called HMLSVMs (Hierarchical Mixing Linear Support Vector Machines) in this paper, which has a hierarchical structure with a mixing linear SVMs classifier at each node and predicts the label of a sample using only a few hyperplanes.
541	Learning to Hash on Partial Multi-Modal Data	Qifan Wang, Luo Si, Bin Shen	But in real applications, it is often the case that every modality suffers from the missing of some data and therefore results in many partial examples,i.e., examples with some modalities missing.In this paper, we present a novel hashing approach to deal with Partial Multi-Modal data.
542	Ranking Preserving Hashing for Fast Similarity Search	Qifan Wang, Zhiwei Zhang, Luo Si	But in many real world applications, ranking measure is important for evaluating the quality of hashing codes.In this paper, we propose a novel Ranking Preserving Hashing (RPH) approach that directly optimizes a popular ranking measure, Normalized Discounted Cumulative Gain (NDCG), to obtain effective hashing codes with high ranking accuracy.
543	A Soft Version of Predicate Invention Based on Structured Sparsity	William Yang Wang, Kathryn Mazaitis, William W. Cohen	Here we suggest a "soft" version of predicate invention: instead of explicitly creating new predicates, we implicitly group closely-related rules by using structured sparsity to regularize their parameters together.
544	Discriminative Unsupervised Dimensionality Reduction	Xiaoqian Wang, Yun Liu, Feiping Nie, Heng Huang	In this paper, we propose a novel graph embedding method for unsupervised dimensionality reduction.
545	A Joint Optimization Framework of Sparse Coding and Discriminative Clustering	Zhangyang Wang, Yingzhen Yang, Shiyu Chang, Jinyan Li, Simon Fong, Thomas S Huang	In this paper, we propose a joint optimization framework in terms of both feature extraction and discriminative clustering.
546	Imaging Time-Series to Improve Classification and Imputation	Zhiguang Wang, Tim Oates	Inspired by recent successes of deep learning in computer vision, we propose a novel framework for encoding time series as different types of images, namely, Gramian Angular Summation/Difference Fields (GASF/GADF) and Markov Transition Fields (MTF).
547	Quantized Correlation Hashing for Fast Cross-Modal Search	Botong Wu, Qiang Yang, Wei-Shi Zheng, Yizhou Wang, Jingdong Wang	In this work, we present a cross-modal hashing approach, called quantized correlation hashing (QCH), which takes into consideration the quantization loss over domains and the relation between domains.
548	Multi-Graph-View Learning for Complicated Object Classification	Jia Wu, Shirui Pan, Xingquan Zhu, Zhihua Cai, Chengqi Zhang	In this paper, we propose to represent and classify complicated objects.
549	Thompson Sampling for Budgeted Multi-Armed Bandits	Yingce Xia, Haifang Li, Tao Qin, Nenghai Yu, Tie-Yan Liu	In this paper, we extend the Thompson sampling to Budgeted MAB, where there is random cost for pulling an arm and the total cost is constrained by a budget.
550	Perception Evolution Network Adapting to the Emergence of New Sensory Receptor	Youlu Xing, Furao Shen, Jinxi Zhao	Perception Evolution Network Adapting to the Emergence of New Sensory Receptor
551	Multi-view Self-Paced Learning for Clustering	Chang Xu, Dacheng Tao, Chao Xu	To overcome this problem, we present a new multi-view self-paced learning (MSPL) algorithm for clustering, that learns the multi-view model by not only progressing from ‘easy’ to ‘complex’ examples, but also from ‘easy’ to ‘complex’ views.
552	Ice-Breaking: Mitigating Cold-Start Recommendation Problem by Rating Comparison	Jingwei Xu, Yuan Yao, Hanghang Tong, Xianping Tao, Jian Lu	In this paper, we propose a novel rating comparison strategy (RaPare) to break this ice barrier.
553	Scalable Maximum Margin Matrix Factorization by Active Riemannian Subspace Search	Yan Yan, Mingkui Tan, Ivor Tsang, Yi Yang, Chengqi Zhang, Qinfeng Shi	To address these two challenges, in this paper, we formulate M3F with a known number of latent factors as the Riemannian optimization problem on a fixed-rank matrix manifold and present a block-wise nonlinear Riemannian conjugate gradient method to solve it efficiently.
554	Deep Convolutional Neural Networks on Multichannel Time Series for Human Activity Recognition	Jianbo Yang, Minh Nhut Nguyen, Phyo Phyo San, Xiao Li Li, Shonali Krishnaswamy	In this paper, we propose a systematic feature learning method for HAR problem.
555	Accelerated Inexact Soft-Impute for Fast Large-Scale Matrix Completion	Quanming Yao, James T. Kwok	In this paper, we show that Soft-Impute can indeed be accelerated without compromising the “sparse plus low-rank” structure.
556	Unsupervised Machine Condition Monitoring Using Segmental Hidden Markov Models	Chao Yuan	We propose an unsupervised approach based on segmental hidden Markov models.
557	Matrix Factorization with Scale-Invariant Parameters	Guangxiang Zeng, Hengshu Zhu, Qi Liu, Ping Luo, Enhong Chen, Tong Zhang	To this end, in this paper we propose a scale-invariant parametric MF method, where a set of scale-invariant parameters is defined for model complexity regularization.
558	A Direct Boosting Approach for Semi-supervised Classification	Shaodan Zhai, Tian Xia, Zhongliang Li, Shaojun Wang	We introduce a semi-supervised boosting approach (SSDBoost), which directly minimizes the classification errors and maximizes the margins on both labeled and unlabeled samples, without resorting to any upper bounds or approximations.
559	Increasingly Cautious Optimism for Practical PAC-MDP Exploration	Liangpeng Zhang, Ke Tang, Xin Yao	We propose the principle of Increasingly Cautious Optimism (ICO) to automatically cut off unnecessarily cautious exploration, and apply ICO to R-MAX and V-MAX, yielding two new strategies, namely Increasingly Cautious R-MAX (ICR) and Increasingly Cautious V-MAX (ICV).
560	Towards Class-Imbalance Aware Multi-Label Learning	Min-Ling Zhang, Yu-Kun Li, Xu-Ying Liu	In this paper, a new multi-label learning approach named Cross-Coupling Aggregation (COCOA) is proposed, which aims at leveraging the exploitation of label correlations as well as the exploration of class-imbalance.
561	Solving the Partial Label Learning Problem: An Instance-Based Approach	Min-Ling Zhang, Fei Yu	In this paper, an instance-based approach named IPAL is proposed by directly disambiguating the candidate label set.
562	Multi-Task Multi-View Clustering for Non-Negative Data	Xianchao Zhang, Xiaotong Zhang, Han Liu	In this paper, for non-negative data (e.g., documents), we introduce a multi-task multi-view clustering (MTMVC) framework which integrates within-view-task clustering, multi-view relationship learning and multi-task relationship learning.
563	Semi-Supervised Multi-Label Learning with Incomplete Labels	Feipeng Zhao, Yuhong Guo	In this paper, we propose a novel semi-supervised multi-label method that integrates low-rank label matrix recovery into the manifold regularized vector-valued prediction framework to address multi-label learning with incomplete labels.
564	Self-Adaptive Hierarchical Sentence Model	Han Zhao, Zhengdong Lu, Pascal Poupart	As an effort towards this goal we propose a self-adaptive hierarchical sentence model (AdaSent).
565	Dual-Regularized Multi-View Outlier Detection	Handong Zhao, Yun Fu	Unfortunately, this kind of outlier is neglected by all the existing multi-view outlier detection methods, consequently their outlier detection performances are dramatically harmed.In this paper, we propose a novel Dual-regularized Multi-view Outlier Detection method (DMOD) to detect both kinds of anomalies simultaneously.
566	Mobile Query Recommendation via Tensor Function Learning	Zhou Zhao, Ruihua Song, Xing Xie, Xiaofei He, Yueting Zhuang	In this paper, we introduce the problem of query recommendation on mobile devices and model the user-location-query relations with a tensor representation.
567	Instance-Wise Weighted Nonnegative Matrix Factorization for Aggregating Partitions with Locally Reliable Clusters	Xiaodong Zheng, Shanfeng Zhu, Junning Gao, Hiroshi Mamitsuka	We propose a new nonnegative matrix factorization (NMF)-based method, in which locally reliable clusters are explicitly considered by using instance-wise weights over clusters.
568	MUVIR: Multi-View Rare Category Detection	Dawei Zhou, Jingrui He, K. Seluk Candan, Hasan Davulcu	To address the problem of multi-view rare category detection, in this paper, we propose a novel framework named MUVIR.
569	Recovery of Corrupted Multiple Kernels for Clustering	Peng Zhou, Liang Du, Lei Shi, Hanmo Wang, Yi-Dong Shen	In this paper, we propose a novel method for learning a robust yet low-rank kernel for clustering tasks.
570	Learning a Robust Consensus Matrix for Clustering Ensemble via Kullback-Leibler Divergence Minimization	Peng Zhou, Liang Du, Hanmo Wang, Lei Shi, Yi-Dong Shen	In this paper, we propose a novel robust clustering ensemble method.
571	Supervised Representation Learning: Transfer Learning with Deep Autoencoders	Fuzhen Zhuang, Xiaohu Cheng, Ping Luo, Sinno Jialin Pan, Qing He	In this paper, we propose a supervised representation learning method based on deep autoencoders for transfer learning.
572	Adaptive Dropout Rates for Learning with Corrupted Features	Jingwei Zhuo, Jun Zhu, Bo Zhang	In this paper, we present a Bayesian feature noising model that flexibly allows for dimension-specific or group-specific noise levels, and we derive a learning algorithm that adaptively updates these noise levels.
573	Evolving Families of Shapes	Filipe Assunção, João Correia, Pedro Martins, Penousal Machado	An evolutionary approach for the creation of such families of shapes, where each genotype encodes a visual language by means of a non-deterministic grammar is explored.
574	Max Order: A Tale of Creativity	Fiammetta Ghedini, François Pachet, Pierre Roy	We present a graphic novel project aiming at illustrating current research results and issues regarding the creative process and its relation with artificial intelligence.
575	Modelling High-Dimensional Sequences with LSTM-RTRBM: Application to Polyphonic Music Generation	Qi Lyu, Zhiyong Wu, Jun Zhu, Helen Meng	We propose an automatic music generation demo based on artificial neural networks, which integrates the ability of Long Short-Term Memory (LSTM) in memorizing and retrieving useful history information, together with the advantage of Restricted Boltzmann Machine (RBM) in high dimensional data modelling.
576	Capturing a Musician’s Groove: Generation of Realistic Accompaniments from Single Song Recordings	Mathieu Ramona, Giordano Cabral, François Pachet	We show that working on accompaniment requires a special care about temporal deviations at the border of the sliced chunks, because they make most of the rhythmic groove.
577	Constitutive and Regulative Specifications of Commitment Protocols: A Decoupled Approach (Extended Abstract)	Matteo Baldoni, Cristina Baroglio, Elisa Marengo, Viviana Patti	We propose a definition of commitment-based interaction protocols, characterized by the decoupling of the constitutive and the regulative specifications, where the latter is explicitly represented based on constraints among commitments.
578	The Arcade Learning Environment: An Evaluation Platform for General Agents (Extended Abstract)	Marc Bellemare, Yavar Naddaf, Joel Veness, Michael Bowling	In this extended abstract we introduce the Arcade Learning Environment (ALE): both a challenge problem and a platform and methodology for evaluating the development of general, domain-independent AI technology.
579	Influencing Individually: Fusing Personalization and Persuasion (Extended Abstract)	Shlomo Berkovsky, Jill Freyne, Harri Oinas-Kukkonen	Likewise, personalized technologies could cash in on increased successes, in terms of user satisfaction, revenue, and user experience, if their services used persuasive techniques.
580	Developing Corpora for Sentiment Analysis: The Case of Irony and Senti-TUT (Extended Abstract)	Cristina Bosco, Viviana Patti, Andrea Bolioli	This paper focusses on the main issues related to the development of a corpus for opinion and sentiment analysis, with a special attention to irony, and presents as a case study Senti-TUT, a project for Italian aimed at investigating sentiment and irony in social media. We present the Senti-TUT corpus, a collection of texts from Twitter annotated with sentiment polarity.
581	Data Complexity of Query Answering in Description Logics (Extended Abstract)	Diego Calvanese, Giuseppe De Giacomo, Domenico Lembo, Maurizio Lenzerini, Riccardo Rosati	We study the data complexity of answering conjunctive queries over Description Logic knowledge bases constituted by a TBox and an ABox.
582	Common Sense Reasoning for Detection, Prevention, and Mitigation of Cyberbullying (Extended Abstract)	Karthik Dinakar, Rosalind Picard, Henry Lieberman	We present an approach for cyberbullying detection based on state-of-the-art text classification and a common sense knowledge base, which permits recognition over a broad spectrum of topics in everyday life.
583	Complexity-Sensitive Decision Procedures for Abstract Argumentation (Extended Abstract)	Wolfgang Dvořák, Matti Järvisalo, Johannes Peter Wallner, Stefan Woltran	In this work, we present a generic approach for reasoning over AFs, based on the novel concept of complexity-sensitivity.
584	The Complexity of Manipulative Attacks in Nearly Single-Peaked Electorates (Extended Abstract)	Piotr Faliszewski, Edith Hemaspaandra, Lane A. Hemaspaandra	The Complexity of Manipulative Attacks in Nearly Single-Peaked Electorates (Extended Abstract)
585	kLog: A Language for Logical and Relational Learning with Kernels (Extended Abstract)	Paolo Frasconi, Fabrizio Costa, Luc De Raedt, Kurt De Grave	We introduce kLog, a novel language for kernel-based learning on expressive logical and relational representations.
586	Framing Image Description as a Ranking Task: Data, Models and Evaluation Metrics (Extended Abstract)	Micah Hodosh, Peter Young, Julia Hockenmaier	We introduce a new dataset of images paired with multiple descriptive captions that was specifically designed for these tasks.
587	Measuring and Recommending Time-Sensitive Routes from Location-based Data	Hsun-Ping Hsieh, Cheng-Te Li, Shou-De Lin	This paper proposes a system, TimeRouter, to recommend time-sensitive trip routes consisting of a sequence of locations with associated time stamps based on knowledge extracted from large-scale location check-in data.
588	Algorithm Runtime Prediction: Methods and Evaluation (Extended Abstract)	Frank Hutter, Lin Xu, Holger Hoos, Kevin Leyton-Brown	In this extended abstract of our 2014 AI Journal article of the same title, we summarize existing models and describe new model families and various extensions.
589	Phrase Detectives: Utilizing Collective Intelligence for Internet-Scale Language Resource Creation (Extended Abstract)	Massimo Poesio, Jon Chamberlain, Udo Kruschwitz, Livio Robaldo, Luca Ducceschi	The paper gives an overview of all aspects of Phrase Detectives, from the design of the game and the methods used, to the results obtained so far.
590	Norms as a Basis for Governing Sociotechnical Systems: Extended Abstract	Munindar P. Singh	We develop an approach for governance based on a computational representation of norms.
591	Continuous Body and Hand Gesture Recognition for Natural Human-Computer Interaction: Extended Abstract	Yale Song, Randall Davis	We present a new approach to gesture recognition that tracks body and hands simultaneously and recognizes gestures continuously from an unsegmented and unbounded input stream.
592	On the Testability of BDI Agent Systems (Extended Abstract)	Michael Winikoff, Stephen Cranefield	In this paper we examine this "obvious intuition" in the case of Belief-Desire-Intention (BDI) agents, by analysing the number of paths through BDI goal-plan trees.
593	Inapproximability of Treewidth and Related Problems (Extended Abstract)	Yu Wu, Per Austrin, Toniann Pitassi, David Liu	In this paper, we study the approximability of a number of graph problems: Treewidth and Pathwidth of graphs, Minimum Fill-In, and a variety of different graph layout problems such as Minimum Cut Linear Arrangement.
594	Feature Ensemble Plus Sample Selection: Domain Adaptation for Sentiment Classification (Extended Abstract)	Rui Xia, Chengqing Zong, Xuelei Hu, Erik Cambria	In this work, we propose a joint approach, named feature ensemble plus sample selection (SS-FE), which takes both types of adaptation into account.
595	Using Social Media to Enhance Emergency Situation Awareness: Extended Abstract	Jie Yin, Sarvnaz Karimi, Andrew Lampert, Mark Cameron, Bella Robinson, Robert Power	We present key relevant approaches that we have investigated including burst detection, tweet filtering and classification, online clustering, and geotagging.
596	When Are Description Logic Knowledge Bases Indistinguishable?	Elena Botoeva, Roman Kontchakov, Vladislav Ryzhikov, Frank Wolter, Michael Zakharyaschev	We study the combined and data complexity of this inseparability problem for fragments of Horn-ALCHI, including the description logics underpinning OWL 2 QL and OWL 2 EL.
597	Description Logic Based Dynamic Systems: Modeling, Verification, and Synthesis	Diego Calvanese, Giuseppe De Giacomo, Marco Montali, Fabio Patrizi	In this paper, we overview the recently introduced general framework of Description Logic Based Dynamic Systems, which leverages Levesque’s functional approach to model systems that evolve the extensional part of a description logic knowledge base by means of actions.
598	Exploiting Separability in Multiagent Planning with Continuous-State MDPs (Extended Abstract)	Jilles Steeve Dibangoye, Christopher Amato, Olivier Buffet, François Charpillet	In this paper, we show that, under certain separability conditions of the optimal value function, the scalability of this approach can increase considerably.
599	Trust-Guided Behavior Adaptation Using Case-Based Reasoning	Michael Floyd, Michael Drinkwater, David Aha	To help a robot integrate itself with a human team, we present an agent algorithm that allows a robot to estimate its trustworthiness and adapt its behavior accordingly.
600	Adapting to User Preference Changes in Interactive Recommendation	Negar Hariri, Bamshad Mobasher, Robin Burke	We use a multi-armed bandit strategy to model this online learning problem and we propose techniques for detecting changes in user preferences.
601	Near-Optimal Approximation Mechanisms for Multi-Unit Combinatorial Auctions	Piotr Krysta, Orestis Telelis, Carmine Ventre	Our objective is to determine allocations of multisets that maximize the Social Welfare approximately.
602	How to Define Certain Answers	Leonid Libkin	We argue that this “one-size-fits-all” definition can often lead to counterintuitive or just plain wrong results, and propose an alternative framework for defining certain answers.
603	Firefly Monte Carlo: Exact MCMC with Subsets of Data	Dougal Maclaurin, Ryan Prescott Adams	Here we present Firefly Monte Carlo (FlyMC) MCMC algorithm with auxiliary variables that only queries the likelihoods of a subset of the data at each iteration yet simulates from the exact posterior distribution.
604	Matching and Grokking: Approaches to Personalized Crowdsourcing	Peter Organisciak, Jaime Teevan, Susan Dumais, Robert C. Miller, Adam Tauman Kalai	We introduce and evaluate two methods for personalized crowdsourcing: taste-matching for finding crowd workers who are similar to the requester, and taste-grokking, where crowd workers explicitly predict the requester’s tastes.
605	Heuristics for Cost-Optimal Classical Planning Based on Linear Programming	Florian Pommerening, Gabriele Roger, Malte Helmert, Blai Bonet	We cover several interesting heuristics of this type by a common framework that fixes the objective function of the linear program.
606	Reasoning with Probabilistic Ontologies	Fabrizio Riguzzi, Elena Bellodi, Evelina Lamma, Riccardo Zese	In this paper we discuss approaches for performing inference from probabilistic ontologies following the DISPONTE semantics.
607	Examples and Tutored Problems: Adaptive Support Using Assistance Scores	Amir Shareghi Najar, Antonija Mitrovic, Bruce McLaren	In this paper we present a study that compares learning from a fixed sequence of alternating worked examples and tutored problem solving to a strategy that adaptively decides how much assistance to provide to the student.
608	Max Is More than Min: Solving Maximization Problems with Heuristic Search	Roni Stern, Scott Kiesel, Rami Puzis, Ariel Felner, Wheeler Ruml	In this paper, we investigate the complementary setting where a solution of high reward is preferred (MAX problems).
609	Speedy versus Greedy Search	Christopher Makoto Wilt, Wheeler Ruml	When an optimal solution is not required, satisficing search methods such as greedy best-first search are often used to find solutions quickly.
610	Online Fair Division	Martin Damyanov Aleksandrov	In this abstract, we give a formulation of this real-world online fair division problem that the food banks face every day.
611	Expressive Rule-Based Stream Reasoning	Harald Beck	This thesis aims to advance the theoretical foundations underlying diverse stream reasoning approaches and to convert obtained insights into a prototypical expressive rule-based reasoning system that is lacking to date.
612	Graph Construction for Semi-Supervised Learning	Lilian Berton, Alneu de Andrade Lopes	This PhD project aims to study this issue and proposes new methods for graph construction from ï¬≠at data and improves the performance of the graph-based algorithms.
613	Stochastic Density Ratio Estimation and Its Application to Feature Selection	Igor Braga	In this work, we deal with a relatively new statistical tool in machine learning: the estimation of the ratio of two probability densities, or density ratio estimation for short.
614	Encoding and Combining Knowledge to Speed up Reinforcement Learning	Tim Brys	We contribute in several ways, proposing novel approaches to transfer learning and learning from demonstration, as well as an ensemble approach to combine knowledge from various sources.
615	A Cognitively Inspired Approach for Knowledge Representation and Reasoning in Knowledge-Based Systems	Joel Luis Carbonera, Mara Abel	In this thesis, I investigate a hybrid knowledge representation approach that combines classic knowledge representations, such as rules and ontologies, with other cognitively plausible representations, such as prototypes and exemplars.
616	Distribution of UCT and Its Ramifications	Marc Yu-San Chee	I have identified issues with chunking in UCT, created by some forms of parallelisation, and developed a solution to this involving buffering of simulations that appear “out of order” and reevaluation of propagation data.
617	A Distributed Platform to Ease the Development of Recommendation Algorithms on Large-Scale Graphs	Alejandro Corbellini	In this thesis, a platform for graph storage and processing named Graphly is proposed along with GraphRec, an API for easy specification of recommendation algorithms.
618	Models for Conditional Preferences as extensions of CP-nets	Cristina Cornelio	This paper presents two frameworks that generalize Conditional Preference networks (CP-nets).
619	Information Extraction of Texts in the Biomedical Domain	Viviana Cotik	The main goal of this research is to develop a method to identify whether medical reports of imaging studies (usually called radiology reports) written in Spanish are important (in the sense that they have non-negated pathological findings) or not.
620	Learning Efficient Logic Programs	Andrew Cropper	We describe an algorithm proven to learn optimal resource complexity robot strategies, and we propose future work to generalise this approach to a broader class of logic programs.
621	RoTuEl: A Semi-Automated Method for Labeling Political Tweets	Wilton de Paula Filho, Ana Cristina Bicharra Garcia	Therefore, the authors of this paper propose to develop a semi-automated model for labeling political tweets.
622	Bipartite Graph for Topic Extraction	Thiago de Paulo Faleiros, Alneu de Andrade Lopes	This paper presents a bipartite graph propagation method to be applied to different tasks in the machine learning unsupervised domain, such as topic extraction and clustering.
623	Statistical Relational Learning Towards Modelling Social Media Users	Golnoosh Farnadi	In this work, we introduce a new statistical relational learning (SRL) framework suitable for this purpose, which we call PSL^Q.
624	On the Static Analysis for SPARQL Queries Using Modal Logic	Nicola Guido	We study static analysis techniques for SPARQL, the standard language for querying Semantic Web data.
625	Exploiting Trust Information to Cope with Malicious Entities in Multi-Agent Systems	Athirai A. Irissappane	More specifically, we focus on evaluating trust relationships between the agents in multi-agent e-marketplaces and sensor networks and aim to address the following problems: 1) how to identify a trustworthy (good quality) agent; 2) how to cope with dishonest advisors i.e., agents who provide misleading opinions about others.
626	Artificial Prediction Markets for Online Prediction	Fatemeh Jahedpari	In this dissertation, we propose an online learning technique to predict a value of a continuous variable by (i) integrating a set of data streams from heterogeneous sources with time varying compositions including (a) changing the quality of data streams, (b) addition or deletion of data streams (ii) integrating the results of several analysis algorithms for each data source when the most suitable algorithm for a given data source is not known a priori (iii) dynamically weighting the prediction of each analysis algorithm and data source on the system prediction based on their varying quality.
627	Multi-Robot Exploration with Communication Restrictions	Elizabeth A. Jensen	We present several algorithms to accomplish this exploration, and provide both theoretical proofs and simulation results that show the algorithms will achieve full exploration of an unknown environment even under communication restrictions.
628	Diagnosis of Technical Systems	Roxane Koitz, Franz Wotawa	In the proposed thesis, we investigate techniques addressing these issues.
629	Abstract Argumentation Frameworks — From Theoretical Insights to Practical Implications	Thomas Linsbichler	In other words, the ultimate goal of the thesis is to gain theoretical insights on argumentation semantics in order to employ them in practically efficient reasoning systems for both the evaluation and evolution of AFs.
630	Flexible Scheduling for an Agile Earth-Observing Satelllite	Adrien Maillard	The objective of this work is to give more autonomy to the satellite without compromising the predictability that is needed for some activities.
631	Towards More Practical Reinforcement Learning	Travis Mandel	In this thesis, I examine these two problems, with an eye towards applications to educational games.
632	Using Small Humanoid Robots to Detect Autism in Toddlers	Marie D. Manner	Our novel contributions will be automatic video processing and automatic behavior classification for clinicians to use with toddlers, validated on a large number of subjects and using a reproducible and portable robotic program for the NAO robot.
633	Automatic Extraction of References to Future Events from News Articles Using Semantic and Morphological Information	Yoko Nakajima	Therefore, I propose a method for automatic extraction of such patterns, applying both grammatical (morphological) and semantic information to represent sentences in morphosemantic structure, and then extract frequent patterns, including those with disjointed elements.
634	Advances in Nonparametric Hypothesis Testing	Aaditya Ramdas	Advances in Nonparametric Hypothesis Testing
635	Efficient Methods for Multi-Objective Decision-Theoretic Planning	Diederik Marijn Roijers	In this project propose new multi-objective planning methods that compute the so-called convex coverage set (CCS): the coverage set for when policies can be stochastic, or the preferences are linear.
636	Automated Agents for Advice Provision	Ariel Rosenfeld	In this thesis, we focus on automated advising agents.
637	Dynamic Execution of Temporal Plans with Sensing Actions and Bounded Risk	Pedro Henrique Santana, Brian C. Williams	We aim at elevating the level in which operators interact with autonomous agents and specify their desired behavior, while retaining a keen sensitivity to risk.
638	An Intelligent and Unified Framework for Multiple Robot and Human Coalition Formation	Sayan Dev Sen	This dissertation develops the intelligent-Coalition Formation framework for Humans and Robots (i-CiFHaR), an intelligent decision making frameworkfor multi-agent coalition formation.
639	Normative Practical Reasoning: An Argumentation-Based Approach	Zohreh Shams	In this thesis we aim: (i) to introduce a model for normative practical reasoning that allows the agents to plan for multiple and potentially conflicting goals and norms at the same time (ii) to implement the model both formally and computationally, (iii) to identify the best plan for the agent to execute by means of argumentation framework and grounded semantics, (iv) to justify the best plan via argumentation-based persuasion dialogue for grounded semantics.
640	Unleashing the Power of Multi-Agent Voting Teams	Leandro Soriano Marcolino	I address all these challenges, with theoretical and experimental contributions.
641	Feature Selection for Multi-Label Learning	Newton Spolaôr, Maria Carolina Monard, Huei Diana Lee	To this end, we propose multi-label feature selection algorithms that take into account label relations.
642	Rational Architecture = Architecture from a Recommender Perspective	Marc van Zee	In this thesis, we develop a framework for reasoning about changing decisions and assumptions, based on logical theories of intentions.
643	Quantifying and Improving the Robustness of Trust Systems	Dongxia Wang	Designers of trust systems propose methods to defend against these attacks.
644	The Spatio-Temporal Representation of Natural Reading	Leila Wehbe	My work is an integrated interdisciplinary effort which employs functional neuroimaging, and revolves around the development of machine learning methods to uncover multi-layer cognitive processes from brain activity recordings.
645	Approximate Algorithms for Stochastic Network Design	Xiaojian Wu	I study the problems of optimizing a range of stochastic processes occurring in networks, such as the information spreading process in a social network, species migration processes in landscape network, virus spreading process in human contact network.
646	Inference and Learning for Probabilistic Description Logics	Riccardo Zese	We presented EDGE that learns the parameters of a DISPONTE KB, and LEAP, that learn the structure together with the parameters of a DISPONTE KB.
647	Improvements of Symmetry Breaking During Search	Zichen Zhu	We propose an adaptation method recursive SBDS (ReSBDS) of ParSBDS which extends ParSBDS to break more symmetry compositions.
648	Activity-Based Scheduling of Science Campaigns for the Rosetta Orbiter	Steve Chien, Gregg Rabideau, Daniel Tran, Martina Troesch, Joshua Doubleday, Federico Nespoli, Miguel Perez Ayucar, Marc Costa Sitja, Claire Vallat, Bernhard Geiger, Nico Altobelli, Manuel Fernandez, Fran Vallejo, Rafael Andres, Michael Kueppers	Rosetta is a European Space Agency (ESA) cornerstone mission that entered orbit around the comet 67P/Churyumov-Gerasimenko in August 2014 and will escort the comet for a 1.5 year nominal mission offering the most detailed study of a comet ever undertaken by humankind.
649	CoBots: Robust Symbiotic Autonomous Mobile Service Robots	Manuela Veloso, Joydeep Biswas, Brian Coltin, Stephanie Rosenthal	In this paper, we identify a few core aspects of our CoBots underlying their robust functionality.