Learning in Multiagent Systems: An Introduction from a Game-Theoretic Perspective

Details Learning in Multiagent Systems: An Introduction from a Game-Theoretic Perspective Learning in Multiagent Systems: An Introduction from a Game-Theoretic Perspective

2003-08-19

arxiv.org/abs/cs/0308030v1

In Eduardo Alonso, editor, Adaptive Agents: LNAI 2636. Springer Verlag, 2003

Computer Science

Multiagent Systems

Scientific paper

We introduce the topic of learning in multiagent systems. We first provide a quick introduction to the field of game theory, focusing on the equilibrium concepts of iterated dominance, and Nash equilibrium. We show some of the most relevant findings in the theory of learning in games, including theorems on fictitious play, replicator dynamics, and evolutionary stable strategies. The CLRI theory and n-level learning agents are introduced as attempts to apply some of these findings to the problem of engineering multiagent systems with learning agents. Finally, we summarize some of the remaining challenges in the field of learning in multiagent systems.

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