Computational Aspects of Cooperative Game Theory (Synthesis Lectures on Artificial Intelligence and Machine Learning)

Chalkiadakis, Georgios; Elkind, Edith; Wooldridge, Michael

ISBN 10: 3031004302 / ISBN 13: 9783031004308

Published by Springer, 2011

About this Item

Seller Inventory # I-9783031004308

Bibliographic Details

• Title: Computational Aspects of Cooperative Game Theory (Synthesis Lectures on Artificial Intelligence and Machine Learning)
• Publisher: Springer
• Publication Date: 2011
• Language: English
• ISBN 10: 3031004302
• ISBN 13: 9783031004308
• Binding: Soft cover
• Condition: New

About this title

Synopsis

Cooperative game theory is a branch of (micro-)economics that studies the behavior of self-interested agents in strategic settings where binding agreements among agents are possible. Our aim in this book is to present a survey of work on the computational aspects of cooperative game theory. We begin by formally defining transferable utility games in characteristic function form, and introducing key solution concepts such as the core and the Shapley value. 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, including, for example, cooperative games defined on networks, as well as general compact representation schemes such as MC-nets and skill games. As a detailed case study, we consider weighted voting games:a widely-used and practically important class of cooperative games that inherently have a natural compact representation. We investigate the complexity of solution concepts for such games, and generalizations of them. 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 some developing topics, applications, and future research directions.

About the Author

Georgios Chalkiadakis is an Assistant Professor at the Department of Electronic and Computer Engineering, Technical University of Crete (TUC). His research interests are in the areas of multiagent systems, algorithmic game theory,and learning,and more specifically,in coalition formation, decision making under uncertainty, and reinforcement learning in multiagent domains. His PhD thesis (University of Toronto, 2007) was nominated for the IFAAMAS-2007 Victor Lesser Distinguished Dissertation Award. Georgios has served as Program Committee member for several top international conferences, and as a reviewer for several top journals in his areas of expertise. Before joining TUC, he was a Research Fellow at the School of Electronics and Computer Science, University of Southampton, UK; and has also worked as a software engineer at the Institute of Computer Science of the Foundation for Research and Technology - Hellas, and as a teacher of informatics in Greek high schools.