Alternate Adaptive Agent Architectures and Behavioral Consequences

David Gaines, Ramakrishnan Pakath

Research output: Contribution to conferencePaperpeer-review

1 Scopus citations


The Learning Classifier System (LCS) and its descendant, XCS, are promising paradigms for adaptive agent construction. Whereas LCS allows classifier payoff predictions to guide system performance, XCS focuses on payoff-prediction accuracy instead, allowing it to evolve “optimal” classifier sets in particular applications requiring rational thought. We examine LCS/XCS performance in artificial situations with broad social/commercial parallels, created using the non-Markov Iterated Prisoner’s Dilemma (IPD) game-playing scenario, where the setting is sometimes asymmetric and where irrationality sometimes pays. We systematically perturb a “conventional” IPD-playing LCS-based agent until it results in a full-fledged XCS-based agent, contrasting the simulated behavior of each LCS variant with the XCS agent in terms of a number of performance measures. Our intent is to examine the XCS paradigm to understand how it better copes with a given situation (if it does) than the LCS perturbations studied.

Original languageEnglish
Number of pages4
StatePublished - 2004
Event10th Americas Conference on Information Systems, AMCIS 2004 - New York, United States
Duration: Aug 6 2004Aug 8 2004


Conference10th Americas Conference on Information Systems, AMCIS 2004
Country/TerritoryUnited States
CityNew York

Bibliographical note

Publisher Copyright:
© 2004, Association for Information Systems. All rights reserved.


  • Adaptive Agents
  • Genetic Algorithms
  • Iterated Prisoner’s Dilemma
  • Learning Classifier Systems
  • XCS

ASJC Scopus subject areas

  • Library and Information Sciences
  • Information Systems
  • Computer Science Applications
  • Computer Networks and Communications


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