T1. Reasoning about Cooperation
Thomas Agotnes, Wiebe van der Hoek and Michael Wooldridge
Abstract: Cooperation sits in the heart of Multi-Agent Systems. It combines strategical (how to cooperate to achieve a certain goal?), social (with whom to cooperate?), competitive (can we join our forces and achieve something against all other's behaviour?), dynamic (how to form a coalition now to achieve something later?) and informational (given the knowledge and ignorance of a coalition, how to proceed?) issues. Research in MAS has experienced a flourishing interest in formal approaches to cooperation, in which languages represent the reasoning of or about agents in coalitions, and models typically represent the effect of agents forming coalitions over time. In the first half or our tutorial, we use Alternating-time Temporal Logic (ATL) as a unifying framework to present several issues in cooperation. ATL is an extension of CTL and allows one to express that a coalition G can, no matter what other agents do, guarantee some temporal property. We show how ATL can be used to represent information of the agents, social behaviour and strategies and actions. We also explain how model checking can be used to verify properties of the overall system in game like scenarios. In the second half, we connect the logical approach with another formal framework for cooperation: game theory. Two links between the logical and the game theoretical approach are drawn. First, we introduce Coalition Logic (CL) which, on the one hand, is equivalent to the next-time fragment of ATL and, on the other hand, is semantically based in a certain type of formal games. Second, we discuss how to express properties of several types of games such as solution concepts in formal logical languages.
T2. Programming Languages and Development Tools for Multi-Agent Systems
Rafael H. Bordini, Mehdi Dastani, João o Leite, Michael Winikoff
Abstract: With the significant advances in the area of autonomous agents and multi-agent systems in the last few years, promising technologies have emerged as a sensible alternative for the design of systems that can operate in complex and dynamic scenarios. However, in order for this technology to become accessible to the multi-agent research community in Academia and practitioners in Industry, it is necessary that programming languages and tools that are appropriate for developing such systems become widely known and thoroughly understood. This course aims at introducing novices, researchers, and developers (from both Academia and Industry) who already have basic notions of multi-agent systems to some of the languages, techniques, and tools that are currently available to support the effective implementation of multi-agent systems.
Cancelled
T3. Intelligent Transportation Systems: What can Agents do?
Franziska Kluegl, Ana L. Bazzan
Abstract: Transportation systems have a huge impact in the economy. Especially regarding vehicular systems, the demand is increasing rapidly and it is not possible to extend the capacity any longer, due to the and social and environmental costs. Therefore, more rational and intelligent uses of the existing capacity are necessary. In particular, techniques from Artificial Intelligence and Multiagent Systems have proven helpful, as they fit very well the concept of Intelligent Transportation Systems (ITS): automated highways, toll-collection systems, in-vehicle GPS and mapping systems, advanced travelers information systems (ATIS), smart control devices and others. Besides, the agent-based paradigm provides powerful modelling tools not only for drivers decision making, but also for integrating several forms of intelligent road system components. In this tutorial we will survey different forms of agent and multiagent based techniques and approaches to traffic simulation, control, and optimization, concentrating not only on driver modeling, but also on modelling other actors in transportation systems, like traffic lights, control centers and pedestrians.
T4. Planning for Multi-Agent Systems
Mathijs de Weerdt, Cees Witteveen
Abstract: By definition, agents are autonomous entities that are able to act. Hence, the process of determining which actions to execute and in which order is considered as an essential property of agents. Within the AI-community such processes have been studied in the context of planning problems. This tutorial will give an introduction to multi-agent planning problems and multi-agent planning techniques, and explain some of these techniques in more detail. In particular we will focus on the coordination of single-agent planners.
T5. Autonomous Bidding Agents
Michael P. Wellman, Peter Stone
Abstract: This tutorial frames and motivates the problem of developing automated trading strategies for electronic markets. E-commerce increasingly makes use of autonomous bidding agents, computer programs that bid in electronic markets without direct human intervention. Automated bidding strategies for an auction of a single good with a known valuation are fairly straightforward to design; designing strategies for simultaneous auctions with interdependent valuations is a more complex undertaking. This tutorial presents algorithmic advances and bidding agent architectures that have emerged from recent work in this fast-growing area of research in academia and industry. It surveys the state-of-the-art in analyzing strategies for basic market games, covers examples of more complex (intractable) market scenarios, and presents a general methodology (empirical and game-theoretic) for trading agent design and analysis. After attending the tutorial, attendees will be equipped to enter into start developing their own agents for participation in future Trading Agent Competitions (TAC), and to fully appreciate research emerging from past competitions (much of which has been presented at AAMAS conferences). Note that TAC was held in conjunction with AAMAS in 2004 and 2006.
Cancelled
T6. Reputation in Online Communities: A Cognitive Approach
Mario Paolucci
Abstract: Reputation is known to be a ubiquitous, spontaneous and highly efficient mechanism of social control in natural societies, influencing both competitive settings and cooperative ones. Reputation currently plays a decisive role in computerized markets, electronic and mobile commerce, virtual communities, and multiagent systems, allowing for a distributed form of infosocial control; today more and more researchers of autonomous agents and multiagent systems are realizing the potential of Reputation-based models. A social cognitive theory of reputation is being developed, The purpose of this tutorial is to introduce participants to the lines of study on the theory of Reputation, with special focus on online communities. Several existing configurations will be examined, and less than obvious aggregate phenomena discussed (ex. "Pollyanna" evaluations) in the light of the theory. Moreover, computational and simulative models of Reputation systems will be presented, supporting parts of theory and/on providing indications on how to design future reputation systems; results from computational models will be compared with selected natural experiments.
T7. Participatory Design of Multi-Agent Systems
Paul Guyot, Yohei Murakami, Eric Platon, Ghislain José Quenum
Abstract: An increasing number of publications in the agent community including at AAMAS, are based on experiments involving human participants and software agents in order to design multi-agent systems and agent-based simulations. In this tutorial, we will explain why and how participation can effectively be used to design multi-agent systems and agent-based simulations. In particular, the presentation will describe the problems that arise with the participatory design of multi-agent systems and how these problems can be solved, based on three different traditions of participatory design of multi-agent systems and agent-based simulations. Since the goal is not only to introduce the details of this technique to the AAMAS audience but also to allow participants of the tutorial to conduct experiments themselves to test, validate and improve their multi-agent systems, we will conduct a small experiment during the tutorial where attendees will take the role of agents taking part in a collaborative task (please bring a Wifi-enabled laptop) and we will introduce Simulaciòn, an open source framework that was developed to conduct such experiments. In order to see more details, please click here.
T8. Agent-Mediated Electronic Negotiation
Han La Poutre, Valentin Robu
Abstract: This tutorial aims to give a broad overview of the state of the art of agent-mediated negotiation (bargaining) mechanisms. The first part of the tutorial will focus on the game-theoretic foundations of electronic negotiation. We review the main concepts from both cooperative and competitive bargaining theory, such as Pareto optimality, the Pareto-efficient frontier as well as the utilitarian, Nash and Kalai-Smorodinsky (egalitarian) solution concepts. Next, we exemplify these concepts in some well-known sequential bargaining games, such as the ultimatum game and the alternating offers game. A particular emphasis will be placed on multi-issue (or multi-attribute) negotiation - a research area that has received significant attention in recent years. We illustrate some of the challenges that arise in modeling negotiations over multiple issues, especially when no information (or only incomplete information) is available about the preferences of the negotiation partner(s). In the second part of the tutorial we present some of the heuristics employed in AI and machine learning research to address the negotiation problem (such as evolutionary computing, fuzzy logic or graph-theoretic models). The tutorial is concluded with an overview of the possible application areas for agent-mediated negotiation techniques, ranging from electronic commerce to distributed logistics and supply chain management. In order to see more details, please click here.
T9: Developing Multiagent Systems: Methods and Techniques
Onn Shehory, Arnon Sturm, Selma Azaiez, Marc-Philippe Huget
Abstract: Agent Oriented Software Engineering (AOSE) is a key factor for introducing agent-based systems to the industry as an engineering approach. At present, the majority of existing agent applications are developed in an ad hoc fashion: little or no rigorous design methodology, limited specification of the requirements, and ad hoc design of agents and of multi-agent systems. By adopting AOSE principles, one gains the advantages of an organized development process. This course will concentrate on methods and techniques for developing multi-agent systems, their applicability, and their use. In particular, the course will: (i) introduce basic concepts of agent-oriented software Engineering; (ii) present several agent-oriented modeling languages and Methodologies; (iii) introduce model-driven development and its use; and (iv) discuss implementation issues of agents and multi-agent systems.
T10: Graphical Models for Multi-Agent Decision-Making
Avi Pfeffer, Kobi Gal
Abstract: Recent work at the interface between artificial intelligence and game theory has provided natural, compact representations for describing multi-agent interaction under uncertainty. These representations use graphical networks that enable to decompose complex decision-making problems into smaller interacting constituents which can be analyzed independently. Applications using graphical networks for decision-making abound, and include military combat simulations and human-computer negotiation. This half-day tutorial will survey these new formalisms, focusing on their expressiveness as knowledge representation languages, and the challenges they pose to inference algorithms. It will show how to construct networks that adequately capture the relationship between agents' beliefs about each others' strategies and the environment. It will present a variety of techniques for efficiently extracting equilibrium strategies for agents that satisfy various conditions. The tutorial is self-contained, and no previous experience to game theory and probabilistic reasoning is required.
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