Interactions in Multiagent Systems: Fairness, Social Optimality and Individual Rationality

This book mainly aims at solving the problems in both cooperative and competitive multi-agent systems (MASs), exploring aspects such as how agents can effectively learn to achieve the shared optimal solution based on their local information and how they can learn to increase their individual utility by exploiting the weakness of their opponents. The book describes fundamental and advanced techniques of how multi-agent systems can be engineered towards the goal of ensuring fairness, social optimality, and individual rationality; a wide range of further relevant topics are also covered both theoretically and experimentally. The book will be beneficial to researchers in the fields of multi-agent systems, game theory and artificial intelligence in general, as well as practitioners developing practical multi-agent systems.

state-of-the-art materials on achieving fairness, social optimality and individual rationality in multi-agent systems Classifies cooperative and competitive multi-agent systems Focuses on a wide variety of multi-agent interaction scenarios represented by different game-theoretic frameworks Includes supplementary material: sn.pub/extras

Inhalt
Introduction.- Background and Previous Work.- Fairness in Cooperative Multiagent Systems.- Social Optimality in Cooperative Multiagent Systems.- Individual Rationality in Competitive Multiagent Systems.- Social Optimality in Competitive Multiagent Systems.- Conclusion.