What evolutionary game theory tells us about multiagent learning

Karl Tuyls*, Simon Parsons

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

60 Citations (Web of Science)

Abstract

This paper discusses If multi-agent learning is the answer what is the question? [Y. Shoham, R. Powers, T. Grenager, If multi-agent learning is the answer, what is the question? Artificial Intelligence 171 (7) (2007) 365-377, this issue] from the perspective of evolutionary game theory. We briefly discuss the concepts of evolutionary game theory, and examine the main conclusions from [Y. Shoham, R. Powers, T. Grenager, If multi-agent learning is the answer, what is the question? Artificial Intelligence 171 (7) (2007) 365-377, this issue] with respect to some of our previous work. Overall we find much to agree with, concluding, however, that the central concerns of multiagent learning are rather narrow compared with the broad variety of work identified in [Y. Shoham, R. Powers, T. Grenager, If multi-agent learning is the answer, what is the question? Artificial Inteligence 171 (7) (2007) 365-377, this issue]. (c) 2007 Elsevier B.V. All rights reserved.

Original languageEnglish
Pages (from-to)406-416
Number of pages11
JournalArtificial Intelligence
Volume171
Issue number7
DOIs
Publication statusPublished - May 2007

Keywords

  • evolutionary game theory
  • replicator dynamics
  • multiagent learning

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