The use of cooperative game theory to study how agents should cooperate and collaborate, along with the related topic of coalition formation, has received growing attention from the multiagent systems, game theory, and electronic commerce communities.  The focus of much of the current work in this area has been on exploring methods by which agents can form coalitions so as to solve problems of joint interest, make group decisions, and distribute gains arising from such cooperation.

The workshop is intended to focus on topics in cooperation in multi­agent systems, cooperative game theory, cooperative solution concepts, coalition formation, and applications.

With the growing prevalance of social interaction through electronic means, trust, reputation, privacy and identity become more and more important. Trust is not just a simple, monolithic concept; it is multi-faceted, operating at many levels of interaction, and playing many roles. Another growing trend is the use of reputation mechanisms, and in particular the interesting link between trust and reputation. Many computational and theoretical models and approaches to reputation have been developed in recent years (for ecommerce, social networks, blogs, etc.). Further, identity and associated trustworthiness must be ascertained for reliable interactions and transactions. Trust is foundational for the notion of agency and for its defining relation of acting "on behalf of". It is also critical for modeling and supporting groups and teams, for both organization and coordination, with the related trade-off between individual utility and collective interest. The electronic medium seems to weaken the usual bonds of social control and the disposition to cheat grows stronger: this is yet another context where trust modeling is critical. The aim of the workshop is to bring together researchers (ideally from different disciplines) who can contribute to a better understanding of trust and reputation in agent societies.

Many large-scale real-world security problems have been successfully modeled as multi-agent security games, and highly scalable algorithmic solutions with software assistants implementing these have been developed and deployed. While there has been significant progress, there still exist many major challenges facing the design of effective approaches to deal with the difficulties in real-world domains. These include building predictive behavioral models for the players, dealing with uncertainties in games, scaling up for large games, and applications of machine learning and multi-agent learning to security, particularly in the context of repeated or stochastic games. This workshop is structured to encourage a lively exchange of ideas to address the above challenges.

Robots are agents, too. Indeed, agent researchers are sometimes inspired by robots, sometimes use robots in motivating examples, and sometimes make contributions to robotics. Both practical and analytical techniques in agent research influence, and are being influenced by, research into autonomous robots and multi-robot systems.

Despite the significant overlap between the multiagent and robotics research areas, roboticists and agents researchers have only a few opportunities to meet and interact. The recently established robotics track at AAMAS is one such opportunity. The goal of the proposed workshop is to extend and widen this opportunity, by offering a forum where researchers in this area of research can interact and present promising innovative research directions, and new results. The workshop is coordinated and associated with the AAMAS robotics track.

The number of novel applications of multi-agent systems has followed an exponential trend over the last few years, ranging from online auction design, through multi-sensor networks, to scheduling of tasks in multi-actor systems. Multi-agent systems designed for all these applications generally involve some form of very hard optimization problems that are substantially different from problems traditionally dealt with in other areas (e.g. industrial processes or scheduling applications).

OptMAS places particular emphasis on distributed constraint reasoning (DCR) approaches, which include the modeling, formulation and solution of DCR problems, including both Distributed Constraint Satisfaction and Optimization Problems. DCR problems arise when pieces of information about variables, constraints or both are relevant to independent but communicating agents. They provide a promising framework to deal with the increasingly diverse range of distributed real world problems emerging from the evolution of computation and communication technologies.

Computational Social Choice (ComSoc) is a rapidly developing field at the intersection of computer science, economics, social choice, and political science. The computer science view of social choice focuses on computational aspects of classical social choice and importing ideas from further afield (i.e. classical social choice) into computer science, broadly. While the surge of research in this area has created dramatic benefits in the areas of matching markets, recommendation systems, and preference aggregation, much of the ComSoc community remains focused on worst case assumptions.

As ComSoc evolves in the coming years there will be an increased need to relax or revise some of the more common assumptions in the field: worst case complexity, complete information, and overly-restricted domains, among others. This means going beyond traditional algorithmic and complexity results and providing a more nuanced look, using real-data, advanced algorithms, and human and agent experimentation to provide a fresh and impactful view of group decision making. This goes hand in hand with highlighting the practical applications of much of the theoretical research — as much of the most impactful work in ComSoc does. It also involves looking at more complex preference aggregation settings that help model real world requirements.

The IDEAS workshop aims to discuss a set of challenging topics related to the application of intelligent agent and multiagent technologies to solving real-world problems and producing deployed applications. The workshop will serve as both (1) a forum for discussing and sharing cutting edge applied agent research (including a place to discuss difficult challenges necessary to overcome to produce real-world systems, as well as lessons learned from initial deployments), as well as (2) an incubator for Innovative Applications Track research to be presented at future AAMAS conferences.

The IDEAS workshop series is of interest to a broad range of researchers and practitioners of intelligent agents and multiagent systems, including (1) computer scientist academics applying their AI/MAS research to real-world problems (e.g., roboticists, software engineers), (2) interdisciplinary researchers combining AI/MAS with various disciplines (e.g., computer aided education, manufacturing, smart energy, e-commerce, e-healthy, survey informatics, intelligent user interfaces), and (3) engineers and scientists from private companies and public organizations performing agent-based research and development, as well as building real commercial systems.

MABS continues to focus on ideas related to the definition of new MAS to address real complex issues, as well as ideas coming from social sciences to MAS as new metaphors to provide insights into MAS theory. Theories, models, analysis, experimental designs, empirical studies, methodological principles, all converge into simulation as a way of achieving explanations and predictions, but also exploration of new hypotheses and conjectures.

The workshop provides a forum for social scientists, agent researchers and developers, and simulation researchers to assess the current state of the art in the modeling and simulation of social systems and MAS, identify where existing approaches can be successfully applied, learn about new approaches and explore future research challenges.

Although much progress has been made in the development of multi-agent systems, the systematic development of large-scale MAS still poses many challenges. Even though various models, techniques and methodologies have been proposed in the literature, researchers and developers are still faced with the common questions: Which architectures are suitable for MAS? How do we specify, design, implement, validate and verify, and evolve our systems? Which notations, models and programming languages are appropriate? Which development tools and frameworks are available? Which processes and methodologies can integrate all of the above and provide a disciplined approach to the rapid development of quality MAS?

EMAS 2016 aims to gather researchers and practitioners in the domains of Agent-Oriented Software Engineering (AOSE), PROgramming Multi-Agent Systems (ProMAS) and Declarative Agent Languages and Technologies (DALT) to present and discuss their research and outcomes in MAS engineering.

Adaptive Learning Agents (ALA) encompasses diverse fields such as Computer Science, Software Engineering, Biology, as well as Cognitive and Social Sciences. The ALA workshop will focus on all aspects of adaptive and learning agents and multiagent systems with a particular emphasis on how to modify established learning techniques and/or create new learning paradigms to address the many challenges presented by complex real-world problems.

The goal of this workshop is to increase awareness and interest in adaptive agent research, encourage collaboration and give a representative overview of current research in the area of adaptive and learning agents and multiagent systems. It aims at bringing together not only scientists from different areas of computer science (e.g., agent architectures, reinforcement learning, and evolutionary algorithms) but also from different fields studying similar concepts (e.g., game theory, bio-inspired control, mechanism design).

Complex Automated Negotiations have been widely studied and are one of the emerging areas of research in the field of Autonomous Agents and Multi-Agent Systems. Research has focused on incorporating different technologies including search, CSP, graphical utility models, Bayesian nets, auctions, utility graphs, optimization and predicting and learning methods. The applications of complex automated negotiations could include e-commerce tools, decision-making support tools, negotiation support tools, collaboration tools, as well as knowledge discovery and agent learning tools.

The goal of this workshop is to bring together researchers from various sub-communities of autonomous agents and multi-agent systems to learn about each other's approaches to the complex negotiation problems, encourage the exchange of ideas between the different areas, and potentially fosters long-term research collaborations to accelerate progress towards scaling up to larger and more realistic applications.

Modern cities have become complex self-organising socio-technical systems. As such, their future is unpredictable beyond broad demographic and land use trends. This uncertainty creates a serious challenge for traditional urban planning as social, economic and land use dynamics dynamically interact at a pace never experienced before.

Spatial micro-simulation and agent-based modelling (ABM) techniques can be used to simulate the actions and interactions of autonomous agents with a view to assessing their effects on the system as a whole. The advantage of such techniques is to allow for the modelling of socio-demographic heterogeneity and the dynamic feedback between urban planning and social responses.

This workshop aims to bring together researchers interested in building large scale agent based urban simulations to discuss issues, techniques and approaches relevant to this effort. We are especially interested in innovative and robust geo-statistical methods for multi-agent-based models.

The pervasiveness of open systems raises a range of challenges and opportunities for research and technological development in the area of autonomous agents and multi-agent systems. Open systems comprise loosely coupled entities interacting within a social space. These entities join the social space in order to achieve some goals that are unattainable by agents in isolation. However, when those entities are autonomous, they might misbehave and, furthermore, in open systems one may not know what entities will be active beforehand, when they may become active or when these entities may leave the system. The challenge in the design and construction of open systems is to devise mechanisms that foster interactions that are conducive to achieving individual or collective goals.

Coordination, organizations, institutions and norms are four key governance elements, and the COIN workshops constitute a space for debate and exploration of these four elements for the design and use of open systems. We seek to attract high-quality papers and an active audience to debate mathematical, logical, computational, methodological, implementational, philosophical and pragmatic issues related to the four aspects of COIN. Of particular interest for the workshop are those papers that articulate a challenging or innovative view.

SPECIAL JOINT SESSION WITH COLLABORATIVE AGENTS RESEARCH & DEVELOPMENT: CARE FOR DIGITAL EDUCATION (CARE)

"CARE for Digital Education" aims to discuss computational models, social computing, and intelligence applied to Digital Education. We are motivated by a recent OECD Report (2015), which points out that despite many years of effort in bringing computing technology to learning practices, there is no appreciable improvement in student achievement. How can Computation Intelligence and Agent-based technology help with that?

We aim to bring together experiences around the utilisation of innovative models of ICT in education, particularly in the fields of: content provisioning and intelligent repositories of learning objects; content composition and recommendation systems for composing personalised and adaptive digital content; content delivery and intelligences for content adaptation, personalised tutoring, and reaction to student behaviour; new forms of automatic assessment, and models to understand the impact of social behaviour, and; learning analytics, models to analyse and understand student behaviour, models to predict student behaviour, and so on.

The ADMI workshop provides a premier forum for sharing research and engineering results, as well as potential challenges and prospects encountered in the respective communities and the coupling between agents and data mining. The workshop welcomes theoretical work and applied dissemination aiming to: (1) explore the integration of agents and data mining towards a super-intelligent system; (2) discuss existing results, new problems, challenges and impact of integration of agent and data mining technologies (3) identify challenges and directions for future research and development on the synergy between agents and data mining. Particularly, ADMI’ 16 will focus on the theme, namely Data-driven Agent Systems, and concentrate on the integration of emerging techniques like social computing, agent behaviorally modeling, big data and cloud computing.

This workshop is on the emergence of intelligence from large-scale complex networked agents. Our brain consists of 50 billion neurons and the neuron network causes emergence of our consciousness and intelligence. And, each human behavior and large-scale complex human network causes emergence of society and social economy. We can see many emergence phenomena like these among the real world. In these cases, not only the dynamics of each neuron or human but also the "network dynamics" of these are important. The aim of this workshop is to investigate the role of networked agents in the emergence of systemic properties, notably emergent intelligence. Focus is on topics such as network formation among agents, the feedback of network structures on agents dynamics, network-based collective phenomena, and emergent problem solving of networked agents.

Rapid increase in the population in urban areas has resulted in social and environmental issues that affect the quality of life of people worldwide. Traffic congestion is one of these issues and intelligent systems are required to control it. Singapore in particular is at the forefront of the effort to use advanced technology to manage and reduce traffic congestion. In particular, Singapore is on the cusp of deploying an island-wide highly pervasive time-distance-place congestion charging program wherein all vehicles that wish to use the roads in Singapore shall be required to be equipped with GNSS/GPS-based tracking and charging units and ultimately charged based on both the nature of the trip made as well as the traffic conditions along those roads at that time. This massive groundbreaking program has spawned a number of new research directions, including those of our proposed workshop.

Game theory, traffic equilibrium and incentive models have a significant role to play in understanding the impacts, and likely outcomes, of such next-generation traffic management schemes. Such models also permit understanding topics such as mitigating ecological impacts of transportation and encouraging mode shift to public transportation. To do so, such models attempt to mimic the way human agents respond to the various state characteristics, such as level of traffic on the road or predictive traffic levels, time of day, day of the week, weather, as well as features of the agents themselves, such as their preferences, values of time, and other disaggregate values. These models can obtain very highly disaggregate data on a scale not experienced to date. Such models take several forms; some represent the human agents as atomic entities each with an individual utility function and others use non-atomic formulations wherein groups are represented. Some have applicability to a dynamic, real-time context with online learning, while others are aimed at providing insights for medium-term horizons.

This workshop seeks to bring together researchers interested in the area of transportation applications of game theory, equilibrium and incentives.

Roman Barták (Charles University)

Action planning is a typical activity of an autonomous agent dealing with finding a plan---a sequence of actions---that leads to a given goal. The tutorial gives an introduction to classical planning methods. It explains the planning problem and its possible representations, gives a survey of major planning techniques, namely state-space planning, plan-space planning, planning with a planning graph, and planning by conversion to other formalisms (SAT and CP), and concludes with the description of possible extensions, some practical aspects, and recent trends.

Amit K. Chopra (Lancaster University),
Munindar P. Singh (North Carolina State University)

Modern IT applications involve multiple people and organizations who interact to exchange information and services. For example, healthcare applications involve patients, physicians, nurses, hospitals, pharmacies, and so on. Today such applications are conceptualized and designed as a single entity system, e.g., as a set of Web services. This tutorial will introduce decentralized multiagent systems (MAS) as a way of realizing modern IT applications. Specifically, the tutorial will introduce interaction protocols as the basic building block for such applications.

The tutorial will describe existing software engineering and artificial intelligence approaches and standards, including UML Sequence Diagrams, FIPA ACL, and multiagent systems programming. It will then introduce topics that specifically address decentralized MAS from the perspective of information modeling and asynchronous enactments, specifically, information-based protocols and commitment protocols. It will also introduce various correctness criteria relevant to such specifications. The tutorial will illustrate the concepts involved with a variety of examples, software demonstrations, and problem sets to be solved by the attendees during the tutorial.

Attendees will appreciate the theoretical foundations of decentralized MAS and learn how to apply interactions protocols toward engineering decentralized MAS. The tutorial builds upon the version presented at AAMAS 2015, where it attracted more than 50 attendees.

Samarth Swarup,
Madhav V. Marathe (Virginia Tech)

Many emerging areas of MAS application are centered on social modeling, including computational sustainability and resilience, urban computing, disaster response, and more. Complex social systems require detailed, data-driven models at scale to enable forecasting, planning, and intervention modeling. The idea of synthetic population modeling, which emerged in transportation modeling and has since been applied to several domains including infrastructure modeling, computational epidemiology, and others, is an important methodology for modeling of complex social systems. This tutorial will introduce MAS researchers to the techniques for generating synthetic populations by integrating multiple sources of data, including census data, activity and time-use data, mobility data, and more. We will cover various learning and optimization methods that are necessary to this process. We will also describe various applications and provide pointers to the literature for further reading.

John P. Dickerson,
Tuomas Sandholm (Carnegie Mellon University)

This tutorial covers past and current research in organ exchange, a method by which patients in need of a organ can swap willing but incompatible donors. Throughout, it also gives a higher-level overview of the steps taken to translate a purely academic idea into a large fielded health-care system. The tutorial focuses on the computational aspects of organ exchange, starting by introducing past research that has now been implemented in real-world kidney exchange (or deemed impractical and not yet fielded) and then by covering the current research problems available at the intersection of AI, optimization, and economics. It especially dives into the computational methods developed and used to solve extremely large discrete optimization problems that reflect kidney exchange, along with the interplay between modeling decisions, computational tractability, exchange efficiency, equity, dynamism in matching, and a variety of other real-world constraints and considerations. While we focus on kidney exchange, research toward exchanges for other organs such as livers and lungs, as well as cross-organ exchanges, will also be covered.

Daan Bloembergen (University of Liverpool),
Tim Brys (Vrije Universiteit Brussel), and Ann Nowé (Vrije Universiteit Brussel)

Reinforcement learning (RL) is an important and fundamental topic within agent-based research, both in a single-agent setting, as well as in multi-agent domains (MARL). After giving successful tutorials on this topic at EASSS 2004 (the European Agent Systems Summer School), ECML 2005, ICML 2006, EWRL 2008, AAMAS 2009-2013, and ECAI 2013, with different collaborators, we now propose a revised and updated tutorial, covering both theoretical as well as practical aspects of single-agent and multi-agent RL.

Participants will be taught the basics of single-agent reinforcement learning and the associated theoretical convergence guarantees related to Markov decision processes. We will discuss how an agent’s learning can be improved by incorporating prior knowledge through reward shaping, and how to extend the RL framework to handle multiple objectives simultaneously. We then move from single-agent to multi-agent RL, and outline why convergence guarantees no longer hold in a setting where multiple agents learn. We will explain practical approaches on how to scale single agent reinforcement learning to these situations where multiple agents influence each other and introduce a framework, based on game theory and evolutionary game theory, that allows thorough analysis of the dynamics of multi-agent learning. Several research applications of MARL will be outlined in detail. The tutorial will include a practical hands-on session, where participants can experience the viability of reinforcement learning in several key application domains.

Paul S. Rosenbloom,
Volkan Ustun (USC Institute for Creative Technologies)

Sigma (Σ) is a nascent cognitive system---i.e., the beginnings of an integrated computational model of intelligent behavior---built around an eponymous cognitive architecture (a hypothesis about the fixed structure underlying cognition). As such, it is intended ultimately to support the real-time needs of intelligent agents, robots and virtual humans. Its development is driven by four desiderata---grand unification, generic cognition, functional elegance, and sufficient efficiency---plus a unique blend of ideas from over thirty years of independent work in the areas of cognitive architectures and graphical models. Work to date on Sigma has covered aspects of learning and memory, perception and attention, reasoning and problem solving, speech and language, and social and affective cognition. It has also involved the development of multiple distinct types of intelligent agents and virtual humans. This tutorial covers the rationale behind Sigma, the basics of its design and operation, and a number of the results that have been generated to date with it.

Munindar P. Singh (North Carolina State University),
Amit K. Chopra (Lancaster University)

This tutorial introduces the Internet of Things (IoT), a rapidly expanding technology area, based on advances in embedded computing, communications, storage, and computing. This tutorial describes how ideas from artificial intelligence---specifically, multiagent systems---can support the core challenges of IoT once we get beyond the infrastructure. It also introduces the research advances needed in the relevant areas to help make the IoT practical.

The tutorial is presented at a senior undergraduate student level. It is accessible to developers from industry and to students. Typical attendees for our past tutorials have been researchers and practitioners from industry and government, developers, graduate and senior undergraduate students, and university faculty.

Full Papers

Extended Abstracts

• 2: Decision Theoretic Norm-Governed Planning
  Luca Gasparini, Timothy Norman, Martin Kollingbaum, Liang Chen
• 6: Egalitarianism of Random Assignment Mechanisms
  Haris Aziz, Jiashu Chen, Aris Filos-Ratsikas, Simon Mackenzie, Nicholas Mattei
• 21: Strategy-Proof Data Auctions with Negative Externalities
  Xiang Wang, Zhenzhe Zheng, Fan Wu, Xiaoju Dong, Shaojie Tang, Guihai Chen
• 24: Appraisal in Human-Robot Collaboration
  Mahni Shayganfar, Charles Rich, Candace L. Sidner
• 32: How testable are BDI agents? An analysis of branch coverage
  Michael Winikoff
• 35: Simulation of agent-rescuers behaviour in emergency based on modified fuzzy clustering
  Andranik Akopov, Armen Beklaryan
• 45: Digital Good Exchange
  Wenyi Fang, Pingzhong Tang, Song Zuo
• 47: Distributed search for pure Nash equilibria in Graphical Games
  Omer Litov, Amnon Meisels
• 48: Multi-Agent Behavior-Based Policy Transfer
  Geoff Nitschke, Sabre Didi
• 50: Wheeled Robots playing Chain Catch: Strategies and Evaluation
  Garima Agrawal, Kamal Karlapalem
• 53: Hierarchical Approach to Transfer of Control in Semi-Autonomous Systems
  Kyle Wray, Luis Pineda, Shlomo Zilberstein
• 54: A Hybrid Approach for Detecting Fraudulent Medical Insurance Claims
  Chenfei Sun, Yuliang Shi, Qingzhong Li, Lizhen Cui, Han Yu, Chunyan Miao
• 55: Reinforcement Learning algorithms for regret minimization in structured Markov Decision Processes
  Theja Tulabandhula, Prabuchandran K.J., Tejas Bodas
• 61: Game-theoretic modeling of transmission line reinforcements with distributed generation
  Merlinda Andoni, Valentin Robu
• 66: Covering Number: Analyses for Approximate Continuous-state POMDP Planning
  Zhang Zongzhang, Liu Quan
• 68: Trust Management for Composite Services in Distributed Multi-agent Systems with Indirect Ratings
  Tung Nguyen, Quan Bai
• 74: Social Welfare in One-Sided Matching Mechanisms
  George Christodoulou, Aris Filos-Ratsikas, Soren Kristoffer Stiil Frederiksen, Paul Goldberg, Jie Zhang, Jinshan Zhang
• 76: Bribery in k-Approval and k-Veto Under Partial Information
  Dirk Briskorn, Gabor Erdelyi, Christian Reger
• 80: Local Search on Trees and a Framework for Automated Construction Using Multiple Identical Robots
  Trevor Cai, David Zhang, T. K. Satish Kumar, Sven Koenig, Nora Ayanian
• 86: Multi-Agent Continuous Transportation with Online Balanced Partitioning
  Chao Wang, Somchaya Liemhetcharat, Bryan Kian Hsiang Low
• 92: Discrete action spaces cause little loss in single item auctions
  Yicheng Liu, Pingzhong Tang
• 101: An Adaptive Learning Framework for Efficient Emergence of Social Norms
  Chao Yu, Hongtao Lv, Sandip Sen, Jianye Hao, Fenghui Ren, and Rui Liu
• 107: Learning from the Wizard: Programming Social Interaction through Teleoperated Demonstrations
  W. Bradley Knox, Samuel Spaulding, Cynthia Breazeal
• 108: Budget-Constrained Reasoning in Agent Computational Environments
  Stefania Costantini, Andrea Formisano
• 113: Relaxation for Constrained Decentralized Markov Decision Processes
  Jie Xu
• 119: Distributed LNS with Quality Guarantees for Distributed Constraint Optimization
  Ferdinando Fioretto, Federico Campeotto, Agostino Dovier, William Yeoh, Enrico Pontelli
• 128: A Bayesian approach for Learning and Tracking Switching, Non-stationary Opponents
  Pablo Hernandez-Leal, Benjamin Rosman, Matthew Taylor, L. Enrique Sucar , Enrique Munoz de Cote
• 131: Online Posted-Price Mechanism with a Finite Time Horizon
  Bo Zheng, Li Xiao, Guang Yang, Tao Qin
• 135: Modeling Team Formation in Self-assembling Software Development Teams
  Mehdi Farhangian, Martin Purvis, maryam purvis, Tony Savarimuthu
• 139: Analysis of condition for the cooperation achievement on arbitrary networks
  SHOHEI USUI, FUJIO TORIUMI
• 140: Achieving Sustainable Cooperation in Generalized Prisoner's Dilemma with Observation Errors
  Fuuki Shigenaka, Shun Yamamoto, Motohide Seki, Tadashi Sekiguchi, Atsushi Iwasaki, Makoto Yokoo
• 145: An Uncertain Assessment Compatible Incentive Mechanism for Eliciting Continual and Truthful Assessme
  Lie Qu, Yan Wang, Mehmet Orgun
• 146: Truthful Team Formation for Crowdsourcing in Social Networks
  Wanyuan Wang, Zhanpeng He, Peng Shi, Weiwei Wu, Yichuan Jiang
• 149: Convergence of Iterative Voting Under Non-Scoring Rules
  Aaron Koolyk, Jeffrey Rosenschein
• 155: A Hyper-Heuristic Framework for Agent-Based Crowd Modeling and Simulation
  Jinghui Zhong, Wentong Cai
• 170: Emergence of Cooperation in Complex Agent Networks Based on Expectation of Cooperation
  Ryosuke Shibusawa, Tomoaki Otsuka, Toshiharu Sugawara
• 192: Market Share Analysis with Brand Effect
  Zhixuan Fang, Longbo Huang
• 205: Argumentation-Based Reasoning Using Preferences over Sources of Information
  Victor Melo, Alison Panisson, Rafael Bordini
• 218: Real-time Robot Path Planning Using Experience Learning From Common Obstacle Patterns
  Olimpiya Saha, Prithviraj Dasgupta
• 219: Can't Do or Won't Do? Perceived Competence, Warmth, and Cooperativeness in Human--Agent Cooperation
  Philipp Kulms, Nikita Mattar, Stefan Kopp
• 223: Computational Models of Emotion, Personality, and Social Relationships for Interactions in Games
  Andry Chowanda, Peter Blanchfield, Martin Flintham, Michel Valstar
• 237: Multi-Objective Dynamic Dispatch Optimisation using Multi-Agent Reinforcement Learning
  Patrick Mannion, Karl Mason, Sam Devlin, Jim Duggan, Enda Howley
• 260: Sequential Plan Recognition
  Reuth Dekel, Roni Stern, Meir Kalech, Kobi Gal
• 264: Strategyproof Matching with Minimum Quotas and Initial Endowments
  Naoto Hamada, Ryoji Kurata, Suguru Ueda, Takamasa Suzuki, Makoto Yokoo
• 266: Incorporating Observation Error when Modeling Trust Between Multiple Robots in a Common Workspa
  Shreyasha Paudel, Christopher Clark
• 270: Towards Learning from Implicit Human Reward
  Guangliang Li, Hamdi Dibeklioglu, Shimon Whiteson, Hayley Hung
• 276: Influence-Based Opinion Diffusion
  Laurence Cholvy
• 284: Applying DCOP_MST to Teams of Mobile Robots with Directional Sensing Abilities
  Harel Yedidsion, Roie Zivan
• 288: Tracking Performance and Forming Study Groups For Prep Courses Using Probabilistic Graphical Models
  Yoad Lewenberg, Yoram Bachrach, Yair Zick, Jeffrey Rosenschein
• 293: Argumentation for Resolving Privacy Disputes in Online Social Networks
  Nadin Kokciyan, Nefise Gizem Yaglikci, Pinar Yolum
• 294: Methods for finding leader--follower equilibria with multiple followers
  Nicola Basilico, Stefano Coniglio, Nicola Gatti
• 305: Multi-agent mechanism for efficient cooperative use of energy
  Romain CAILLIERE, antoine nongaillard, Samir Aknine
• 316: A Dialogue Protocol to Support Meaning Negotiation
  Gabriele Santos, Valentina Tamma, Terry Payne, Floriana Grasso

Full Papers

Extended Abstracts

• 323: How is cooperation/collusion sustained in repeated multimarket contact with observation errors?
  Atsushi Iwasaki, Tadashi Sekiguchi, Shun Yamamoto, Makoto Yokoo
• 330: Structural Control in Weighted Voting Games
  Anja Rey, Joerg Rothe
• 333: Robots reasoning with cuts and connections: creating and removing entities
  Mihai Pomarlan, Michael Beetz
• 352: Minimising the Rank Aggregation Error
  Mathijs de Weerdt, Enrico Gerding, Sebastian Stein
• 354: Moving Target Defense For Web Applications Using Bayesian Stackelberg Games
  Satya Gautam Vadlamudi, Sailik Sengupta, Subbarao Kambhampati, Marthony Taguinod, Ziming Zhao, Adam Doupé, Gail-Joon Ahn
• 355: Water Resources Systems Operations via Multiagent Negotiation
  Francesco Amigoni, Andrea Castelletti, Paolo Gazzotti, Matteo Giuliani, Emanuele Mason
• 359: Learning to be fair in multiplayer ultimatum games
  Fernando Santos, Francisco C. Santos, Francisco Melo, Ana Paiva, Jorge M. Pacheco
• 363: Verifying Conflicts among Multiple Norms in Multi-agent Systems
  EDUARDO SILVESTRE, VIVIANE SILVA
• 368: A Truthful Mechanism with Biparameter Learning for Online Crowdsourcing
  Satyanath Bhat, Divya Padmanabhan, Shweta Jain, Yadati Narahari
• 372: Probably Approximately Correct Greedy Maximization
  Yash Satsangi, Shimon Whiteson, Frans Oliehoek
• 397: Analysis of Lane Level Dynamics for Emergency Vehicle Traversal
  Akash Agarwal, Praveen Paruchuri
• 404: Games of Influence
  Umberto Grandi, Emiliano Lorini, Laurent Perrussel
• 406: Preference elicitation in matching markets via interviews: A study of offline benchmarks
  Baharak Rastegari, David Manlove, Paul Goldberg
• 412: Robust Influence Maximization
  Meghna Lowalekar, Pradeep Varakantham, Akshat Kumar
• 415: Multi-Agent Cooperative Area Coverage: Case Study Ploughing
  ALIREZA JANANI
• 420: Decentralised Norm Monitoring in Open Multi-Agent Systems
  Natasha Alechina, Joseph Y. Halpern, Ian Kash, Brian Logan
• 421: Optimizing Profit and Efficiency in On-Demand Transport Services
  Malcolm Egan, Michal Jakob, Nir Oren
• 423: Integrating Run-Time Incidents in a Large-Scale Simulated Urban Environment
  Steven De Jong, Alex Klein, Ruben Smelik, Freek van Wermeskerken
• 437: Learning better trading dialogue policies by inferring opponent preferences
  Ioannis Efstathiou, Oliver Lemon
• 438: Efficient Boolean Games Equilibria: A Scalable Approach
  Zohar Komarovsky, Vadim Levit, Tal Grinshpoun, Amnon Meisels
• 447: Coordinating Wind Turbines and Flexible Consumers with Cooperative and Competitive Agents
  Kristof Coninx, Tom Holvoet
• 457: Approximate Plutocratic and Egalitarian Nash Equilibria
  Artur Czumaj, Michail Fasoulakis, Marcin Jurdzinski
• 462: A Core Task Abstraction Approach to Hierarchical Reinforcement Learning
  Zhuoru Li, Akshay Narayan, Tze Yun Leong
• 492: Role Assignment for Game-Theoretic Cooperation
  Catherine Moon, Vincent Conitzer
• 498: Conveying Social Relations in Virtual Agents Through an Emotion Sharing and Response Model
  Nuno Salvador, Joao Dias, Samuel Mascarenhas, Ana Paiva
• 508: Exclusion Method for Finding Nash Equilibrium in Multi-Player Games
  Kimmo Berg, Tuomas Sandholm
• 511: Sum-Product-Max Networks for Tractable Decision Making
  Mazen Melibari, Pascal Poupart, Prashant Doshi
• 513: Knowledge-Enabled Robotic Agents for Shelf Replenishment in Cluttered Retail Environments
  Jan Winkler, Ferenc Balint-Benczedi, Thiemo Wiedemeyer, Michael Beetz, Narunas Vaskevicius, Christian A. Mueller, Tobias Fromm, Andreas Birk
• 526: Communicating Intentions for Coordination with Unknown Teammates
  Trevor Santarra, Arnav Jhala
• 528: OWLS: Observational Wireless Life-enhancing System
  Zheng Hanzhong, Almog Boanos, Jumadinova Janyl
• 531: Supporting group plans in the BDI architecture using coordination middleware
  Stephen Cranefield
• 539: Reputation-based Provider Incentivisation for Provenance Provision
  Lina Barakat, Samhar Mahmoud, Phillip Taylor, Nathan Griffiths, Simon Miles
• 546: The Convergence of Reciprocation
  Gleb Polevoy, Mathijs de Weerdt, Catholijn Jonker
• 548: Interacting with Virtual Agents in Shared Spaces: Single and Joint Effects of Gaze and Proxemics
  Jan Kolkmeier, Jered Vroon, Dirk Heylen
• 553: SOBE: Source Behavior Estimation for Subjective Opinions In Multiagent Systems
  Murat Sensoy, Lance Kaplan, Geeth de Mel, Taha Dogan Gunes
• 558: Boolean Satisfiability Approach to Optimal Multi-agent Path Finding under the Sum of Cost Objective
  Pavel Surynek, Ariel Felner, Roni Stern, Eli Boyarski
• 563: What kind of stories should a virtual human swap?
  Setareh Gilani, Kraig Sheetz, Gale Lucas, David Traum
• 572: Simultaneous Influencing and Mapping Social Networks
  Leandro Soriano Marcolino, Aravind Srinivas Lakshminarayanan, Amulya Yadav , Milind Tambe
• 577: Collaborative Human Task Assignment for Open Systems
  Bin Chen, Adam Eck, Leen-Kiat Soh
• 579: Investigating the Characteristics of One-Sided Matching Mechanisms
  Hadi Hosseini, Kate Larson, Robin Cohen
• 593: A Multi-Agent System for the Deployment of Applications in Ambient Systems
  Piette Ferdinand, Costin Caval, Amal El Fallah Seghrouchni, Patrick Taillibert, Cédric Dinont
• 604: Object-Focused Advice in Reinforcement Learning
  Samantha Krening, Brent Harrison, Karen Feigh, Charles L. Isbell Jr., Andrea Thomaz
• 611: Reinforcement Learning Framework for Modeling Spatial Sequential Decisions under Uncertainty
  Truc Viet Le, Siyuan Liu, Hoong Chuin Lau
• 618: Simulation Summarization
  Nidhi Parikh, Madhav Marathe, Samarth Swarup
• 639: An Autonomous Agent for Learning Spatiotemporal Models of Human Daily Activities
  Shan Gao, Ah-Hwee Tan
• 658: Policy Shaping in Domains with Multiple Optimal Policies
  Himanshu Sahni, Brent Harrison, Kaushik Subramanian, Thomas Cederborg, Charles L. Isbell Jr., Andrea Thomaz
• 665: Using Facial Expression and Body Language to Express Attitude for Non-Humanoid Robot
  Mei Si, J. Dean McDaniel
• 667: Revenue Maximization for Finitely Repeated Ad Auctions
  Jiang Rong, Tao Qin, Bo An, Tie-Yan Liu
• 678: Multi-Option Descending Clock Auction
  Tri-Dung Nguyen, Tuomas Sandholm
• 692: Strategy-proof mechanism design for facility location games: revisited
  Lili Mei, Minming Li, Deshi Ye, Guochuan Zhang
• 696: Stochastic Shortest Path with Energy Constraints in POMDPs
  Tomas Brazdil, Krishnendu Chatterjee, Martin Chmelik, Anchit Gupta, Petr Novotny
• 697: Using environmental patterns to evaluate agent teams performance
  Mariana Franco, Gustavo Campos, Jaime Sichman
• 698: Epistemic boolean games based on a logic of visibility and control
  Andreas Herzig, Emiliano Lorini, Faustine Maffre, Francois Schwarzentruber
• 700: Estimating Second-Order Arguments in Dialogical Settings
  seyed ali hosseini, Sanjay Modgil, Odinaldo Rodrigues