Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations

This exciting and pioneering new overview of multiagent systems, which are online systems composed of multiple interacting intelligent agents, i.e., online trading, offers a newly seen computer science perspective on multiagent systems, while integrating ideas from operations research, game theory, economics, logic, and even philosophy and linguistics. The authors emphasize foundations to create a broad and rigorous treatment of their subject, with thorough presentations of distributed problem solving, game theory, multiagent communication and learning, social choice, mechanism design, auctions, cooperative game theory, and modal logics of knowledge and belief. For each topic, basic concepts are introduced, examples are given, proofs of key results are offered, and algorithmic considerations are examined. An appendix covers background material in probability theory, classical logic, Markov decision processes and mathematical programming. Written by two of the leading researchers of this engaging field, this book will surely serve as THE reference for researchers in the fastest-growing area of computer science, and be used as a text for advanced undergraduate or graduate courses.

Yoav Shoham is Professor of Computer Science at Stanford University, where he has been since receiving his PhD in computer science from Yale University in 1987. Shoham is a Fellow of the Association for Advancement of Artificial Intelligence (AAAI) and charter member of the International Game Theory Society. Aside from authoring four books and numerous other works, he is director of TARK (Theoretical Aspects of Rationality and Knowledge), a non-profit organization.

Kevin Leyton-Brown is an Assistant Professor of Computer Science at the University of British Columbia. His affiliations include the Laboratory for Computational Intelligence (LCI) and the Bioinformatics, and Empirical and Theoretical Algorithmics Laboratory (BETA-Lab) and membership on the editorial board of the Journal of AI Research (JAIR).