Mor, Yishay; Goldman, Claudia V. and Rosenschein, Jeffrey S.
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|DOI (Digital Object Identifier) Link:||https://doi.org/10.1007/3-540-60923-7_26|
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Agents that interact in a distributed environment might increase their utility by behaving optimally given the strategies of the other agents. To do so, agents need to learn about those with whom they share the same world. This paper examines interactions among agents from a game theoretic perspective. In this context, learning has been assumed as a means to reach equilibrium. We analyze the complexity of this learning process. We start with a restricted two-agent model, in which agents are represented by finite automata, and one of the agents plays a fixed strategy. We show that even with this restrictions, the learning process may be exponential in time. We then suggest a criterion of simplicity, that induces a class of automata that are learnable in polynomial time.
|Item Type:||Conference Item|
|Copyright Holders:||1996 Springer-Verlag|
|Extra Information:||Adaption and Learning in Multi-Agent Systems
IJCAI '95 Workshop
Montreal, Canada, August 21, 1995
Gerhard Weil3 Sandip Sen (Eds.)
Berlin : Springer-Verlag, 1996
Lecture Notes in Computer Science 1042
|Keywords:||distributed artificial intelligence; learning; repeated games; automata|
|Academic Unit/School:||Learning Teaching and Innovation (LTI) > Institute of Educational Technology (IET)
Learning Teaching and Innovation (LTI)
|Interdisciplinary Research Centre:||Centre for Research in Education and Educational Technology (CREET)|
|Depositing User:||Yishay Mor|
|Date Deposited:||31 May 2012 10:52|
|Last Modified:||10 Feb 2017 06:45|
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