What evolutionary game theory tells us about multiagent learning

Authors:
Karl Tuyls;Simon Parsons
Affiliations:
Institute for Knowledge and Agent Technology, Maastricht University, The Netherlands;Department of Computer and Information Science, Brooklyn College, City University of New York, 2900 Bedford Avenue, Brooklyn, 11210 NY, USA
Venue:
Artificial Intelligence
Year:
2007

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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].