On ZCS in multi-agent environments

  • Larry Bull
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1498)


This paper examines the performance of the ZCS Michigan-style classifier system in multi-agent environments. Using an abstract multi-agent model the effects of varying aspects of the performance, reinforcement and discovery components are examined. It is shown that small modifications to the basic ZCS architecture can improve its performance in environments with significant inter-agent dependence. Further, it is suggested that classifier systems have characteristics which make them more suitable to such non-stationary problem domains in comparison to other forms of reinforcement learning. Results from the initial use of ZCS as an adaptive economic trading agent within an artificial double-auction market are then presented, with the findings from the abstract model shown to improve the efficiency of the traders and hence the overall market.


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Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Larry Bull
    • 1
  1. 1.Intelligent Computer Systems CentreUniversity of the West of EnglandBristolUK

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