Author： Tuyls Karl Nowe Ann Lenaerts Tom Manderick Bernard
Publisher： Springer Publishing Company
ISSN： 0039-7857
Source： Synthese, Vol.139, Iss.2, 2004-03, pp. : 297-330
Disclaimer: Any content in publications that violate the sovereignty, the constitution or regulations of the PRC is not accepted or approved by CNPIEC.
Abstract
In this paper we revise Reinforcement Learning and adaptiveness in Multi-Agent Systems from an Evolutionary Game Theoretic perspective. More precisely we show there is a triangular relation between the fields of Multi-Agent Systems, Reinforcement Learning and Evolutionary Game Theory. We illustrate how these new insights can contribute to a better understanding of learning in MAS and to new improved learning algorithms. All three fields are introduced in a self-contained manner. Each relation is discussed in detail with the necessary background information to understand it, along with major references to relevant work.
Related content
Comparing Semantics of Logics for Multi-Agent Systems
Synthese, Vol. 139, Iss. 2, 2004-03 ,pp. :
Access to resources
Recommend
Learning to Coordinate; a Recursion Theoretic Perspective
By Montagna Franco Osherson Daniel
Synthese, Vol. 118, Iss. 3, 1999-03 ,pp. :
A Framework for Multi-Agent Belief Revision
Studia Logica, Vol. 67, Iss. 2, 2001-03 ,pp. :
A Formal Model of Multi-Agent Belief-Interaction
Journal of Logic, Language and Information, Vol. 14, Iss. 4, 2005-10 ,pp. :
A Formal Model of Multi-Agent Belief-Interaction
Journal of Logic, Language and Information, Vol. 15, Iss. 4, 2006-11 ,pp. :