Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations Yoav Shoham & Kevin Leyton-Brown

Table of Contents

1. Distributed constraint satisfaction
2. Distributed optimization
3. Introduction to non-cooperative game theory
4. Computing solution concepts of normal-form games
5. Games with sequential actions
6. Richer representations
7. Learning and teaching
8. Communication
9. Aggregating preferences
10. Protocols for strategic agents
11. Protocols for multiagent resource allocation
12. Teams of selfish agents
13. Logics of knowledge and belief
14. Beyond belief.

Multiagent systems combine multiple autonomous entities, each having diverging interests or different information. This overview of the field offers a computer science perspective, but also draws on ideas from game theory, economics, operations research, logic, philosophy and linguistics. It will serve as a reference for researchers in each of these fields, and be used as a text for advanced undergraduate or graduate courses. 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.