Title of article
What evolutionary game theory tells us about multiagent learning Original Research Article
Author/Authors
Karl Tuyls، نويسنده , , Peter McBurney and Simon Parsons ، نويسنده ,
Issue Information
روزنامه با شماره پیاپی سال 2007
Pages
11
From page
406
To page
416
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].
Keywords
Evolutionary game theory , Replicator dynamics , Multiagent learning
Journal title
Artificial Intelligence
Serial Year
2007
Journal title
Artificial Intelligence
Record number
1207536
Link To Document