Learn your opponent’s strategy (in polynomial time)!

Yishay Mor, Claudia V. Goldman, Jeffrey S. Rosenschein

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

11 Scopus citations

Abstract

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.

Original languageAmerican English
Title of host publicationAdaption and Learning in Multi-Agent Systems - IJCAI 1995 Workshop, Proceedings
EditorsGerhard Weib, Sandip Sen
PublisherSpringer Verlag
Pages165-176
Number of pages12
ISBN (Print)9783540609230
DOIs
StatePublished - 1996
EventWorkshop on Adaptation and Learning in Multi-Agent Systems, 1995 held as part of 14th International Joint Conference on Artificial Intelligence, IJCAI 1995 - Montreal, Canada
Duration: 21 Aug 199521 Aug 1995

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1042
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceWorkshop on Adaptation and Learning in Multi-Agent Systems, 1995 held as part of 14th International Joint Conference on Artificial Intelligence, IJCAI 1995
Country/TerritoryCanada
CityMontreal
Period21/08/9521/08/95

Bibliographical note

Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 1996.

Keywords

  • Automata
  • Distributed artificial intelligence
  • Learning
  • Repeated games

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