Multiagent Learning for Open Systems: A Study in Opponent Classification

Michael Rovatsos, Gerhard Weiß, Marco Wolf

Research output: Chapter in Book/Report/Conference proceedingChapter


Open systems are becoming increasingly important in a variety of distributed, networked computer applications. Their characteristics, such as agent diversity, heterogeneity and fluctuation, confront multiagent learning with new challenges. This paper presents the interaction learning meta-architecture InFFrA as one possible answer to these challenges, and introduces the opponent classification heuristic ADHOC as a concrete multiagent learning method that has been designed on the basis of InFFrA. Extensive experimental validation proves the adequacy of ADHOC in a scenario of iterated multiagent games and underlines the usefulness of schemas such as InFFrA specifically tailored for open multiagent learning environments. At the same time, limitations in the performance of ADHOC suggest further improvements to the methods used here. Also, the results obtained from this study allow more general conclusions regarding the problems of learning in open systems to be drawn.
Original languageEnglish
Title of host publicationAdaptive Agents and Multi-Agent Systems
Subtitle of host publicationAdaptation and Multi-Agent Learning
EditorsEduardo Alonso, Daniel Kudenko, Dimitar Kazakov
PublisherSpringer Berlin Heidelberg
Number of pages22
ISBN (Electronic)978-3-540-44826-6
ISBN (Print)978-3-540-40068-4
Publication statusPublished - 2003

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Berlin Heidelberg
ISSN (Print)0302-9743

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