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2007
Springer

What evolutionary game theory tells us about multiagent learning

13 years 11 months ago
What evolutionary game theory tells us about multiagent learning
This paper discusses If multi-agent learning is the answer, what is the question? [Y. Shoham, R. Powers, T. Grenager, If multiagent 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]. © 2007 Elsevier B.V. All rights reserved.
Karl Tuyls, Simon Parsons
Added 08 Dec 2010
Updated 08 Dec 2010
Type Journal
Year 2007
Where AI
Authors Karl Tuyls, Simon Parsons
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