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Karl Tuyls and Simon Parsons. What evolutionary game theory tells us about multiagent learning. Artificial Intelligence, 171(7):406–416, 2007.
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This paper discusses If multi-agent learning is the answer, what is the question? [20]from the perspective of evolutionary game theory. We briefly discuss the concepts ofevolutionary game theory, and examine the main conclusions from [20] with respectto some of our previous work. Overall we find much to agree with, concluding, however,that the central concerns of multiagent learning are rather narrow comparedwith the broad variety of work identified in [20].
@ARTICLE{TuyPar07,
author = {Karl Tuyls and Simon Parsons},
title = {What evolutionary game theory tells us about multiagent learning},
journal = {Artificial Intelligence},
year = {2007},
volume = {171(7)},
pages = {406-416},
abstract = {This paper discusses If multi-agent learning is the answer, what is the question? [20]
from the perspective of evolutionary game theory. We briefly discuss the concepts of
evolutionary game theory, and examine the main conclusions from [20] 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 [20].}
bib2html_pubtype = {Journal},
bib2html_rescat = {Multi-Agent Learning},
owner = {K.Tuyls},
timestamp = {2007.01.16},
}
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