Tutorials
Full-Day Tutorials
Logics
T1Logics and Multi-Agent Programming Languages When designing a multi-agent programming language, researchers and developers must address deep questions such as: what are the basic constituent parts of intelligent agents, the organisation in which they operate, and the environment with which they interact. In particular, how should agents 'think' (e.g., which deliberation strategy should they employ -- should an agent plan a precise sequence of actions in advance or should it adopt an abstract plans with gaps 'to be filled-in later' what relationship should there be between the agent's beliefs and its goals, what are the organisational rules and norms that agents should follow, how can an agent decide whether and to which extent it should respect organisational rules and norms, which resources and services in environment should be used, how to devise organisations and environments to ensure specific properties of multi-agent systems, and so on. In seeking to address these questions, researchers have drawn heavily on formal models of agents and on agent logics, including epistemic logics, logics of action, dynamic logic, coalition logics, game theory, etc. For example, the development of agent programming languages such as AgentSpeak(L) and 2APL were heavily influenced by the BDI (Beliefs, Desires and Intentions) logics developed to understand what an agent's behaviour should be. These interactions have resulted in an extremely fruitful cross fertilisation between work in logic and the design of multi-agent programming languages, and the application of logical techniques to address key practical issues such as the verification of agent programs (i.e., will an agent program meet the specification set out by its developers). This tutorial will address key topics in logics of multi-agent programs including: the Belief Desire Intention model of individual agents; normative models for multi-agent organisations, concurrent game structures for environments, overview of multi-agent programming languages; relationship between the operational semantics of multi-agent programming languages and logics for reasoning about agents' beliefs, intentions, norms, sanctions, and environment interactions; and the verification of multi-agent programs.
Emotions
T2Computational and formal models of cognitive emotions Emotions play a central role in cognition and in social interaction. Their role and integration into multiagent systems and more generally computerized systems are the subject of a lot of research efforts. One of the aims of the research on emotions in the agent field is to improve human interaction with machines by endowing the latter with the capability of understanding human emotions and generating believable behaviour by means of emotional features ('virtual agents' Beyond basic emotions such as joy, sadness, fear and hope, a major topic is the analysis of complex emotions types such as counterfactual émotions (e.g. disappointment, regret, guilt) and social emotions (e.g. shame, envy). The tutorial aims at providing an overview of current computational and formal models of emotion proposed in the area of agents and multiagent systems which are used as an abstract specification for the design of articial agents interacting with humans.
MARL
T3Multi-agent reinforcement learning Participants will learn the core principles of multi-agent reinforcement learning. After a discussion of its challenges, we will explain practical approaches on how to scale single agent reinforcement learning to situations with multiple interacting agents. A framework based on game theory and evolutionary game theory will be used to analyze the learning dynamics, culminating in a taxonomy of learning algorithms. The tutorial will close with a demonstration session, showing the viability of reinforcement learning in several key application domains.
Decisions
T4Decision Making in Multiagent Settings Drawing motivation from search and rescue applications in disaster management, the tutorial will span the range of multiagent interactions of increasing generality, and study a set of optimal and approximate solution techniques to time-extended decision making in these multiagent contexts. This self-contained tutorial will begin with the relevant portions of game theory and culminate with several advanced decision-theoretic models of agent interactions.
Coop GT
T6Computational Aspects of Cooperative Game Theory Cooperative game theory studies the behavior of self-interested agents in strategic settings where binding agreements among agents are possible. We present a survey of work on the computational aspects of cooperative game theory. We begin by formally defining transferable utility games, and introducing key solution concepts for such games. We then discuss two major issues that arise when considering such games from a computational perspective: identifying compact representations for games, and the closely related problem of efficiently computing solution concepts for games. We survey several formalisms for cooperative games that have been proposed in the literature. We briefly discuss games with non-transferable utility and partition function games. We then overview algorithms for identifying welfare-maximizing coalition structures and methods used by rational agents to form coalitions (even under uncertainty), including bargaining algorithms. We conclude by considering applications and future research directions. The tutorial is closely based our new textbook with the same title: http://web.spms.ntu.edu.sg/~eelkind/coopbook/.
Social Laws
T7Social Laws for Multi-Agent Systems Social laws (or normative systems) have emerged as a natural and powerful paradigm for coordinating multi-agent systems. The social laws paradigm exposes the whole spectrum between fully centralised and fully decentralised coordination mechanisms. A social law is, intuitively, a constraint on the behaviour of agents, which ensures that their individual behaviours are compatible. Typically, a social law is imposed off-line, minimising the chances of on-line conflict or the need to negotiate. The tutorial gives an overview of the state-of-the-art in the use of social laws in multi-agent systems.
Equilibrium
T8Equilibrium Computation This tutorial aims at providing a gentle introduction to the computational results for non-cooperative game theory. The tutorial will introduce the basics of non-cooperative game theory: mechanisms in strategic and extensive form and strategies, solution concepts and their motivation, and examples of applications. Then, for the most adopted solution concepts, the computational complexity of verifying a solution, finding an equilibrium, and other advanced results (i.e., searching for optimal equilibria or approximate equilibria) will be discussed and subsequently the main algorithms will be presented with some examples of applications. The tutorial will provide basics, well established results, and recent advancements.
Security
T10Game Theory and Security Game theory is an increasingly important paradigm for modeling security games and decision-making in these domains, including homeland security resource allocation decisions, robot patrolling strategies, and computer network security. This tutorial introduces a wide variety of game-theoretic modeling techniques and algorithms that have been developed in recent years for security problems, and it provides a structured and comprehensive overview of research on security and privacy in computer networks and cyber-physical systems that uses game-theoretic approaches. We present a selected set of works to highlight the application of game theory in addressing different forms of security and privacy problems in communication networks, mobile applications and cyber-physical systems.
Half-Day Tutorials
AMEN
T5Agent-Mediated Electronic Negotiation This tutorial aims to give a broad overview of state of the art in agent-mediated negotiation. The tutorial will focus on the game-theoretic foundations of electronic negotiations. We review the main concepts from both cooperative and competitive bargaining theory, such as Pareto optimality, the Pareto-efficient frontier as well as utilitarian, Nash and Kalai-Smorodinsky (egalitarian) solution concepts. We discuss and compare games with complete and with incomplete information. Next, we exemplify these concepts through some well-known sequential bargaining games, such as the ultimatum 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 from the multi-agent community. We discuss 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), as well as some of the heuristics employed in AI and machine learning research to solve this problem. The second part of the tutorial focuses on multi-issue negotiations which may have realistic limitations like time-constraints, computational tractablility, private information issues, online negotiations, etc.
Human-Comp
T9Designing Computer Agents for Human-Computer Decision-Making Settings in which humans and computers make decisions together are becoming increasingly prevalent (e.g., electronic commerce, intelligent tutors, office assistants, negotiation training). How to design effective computer agents in these settings requires understanding the social and psychological factors that affect human behavior, which often transcend our formal models of a “rational” actor. This half-day tutorial will focus on computational representations, algorithms and empirical methodologies for meeting this challenge. It will (1) present historical and contemporary views on human decision-making from behavioral economics and cognitive psychology, (2) show how these results can inform computational models of behavior, and (c) present empirical methodologies for facilitating the design and evaluation of computational strategies in different types of environments.