Enhancing Learning Capabilities by XCS with Best Action Mapping

  • Masaya Nakata
  • Pier Luca Lanzi
  • Keiki Takadama
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7491)


This paper proposes a novel approach of XCS called XCS with Best Action Mapping (XCSB) to enhance the learning capabilities of XCS. The feature of XCSB is to learn only best actions having the highest predicted payoff with the high accuracy unlike XCS which learns actions having the highest and lowest predicted payoff with the high accuracy. To investigate the effectiveness of XCSB, we applied XCSB to two benchmark problems: multiplexer problem as a single step problem and maze problem as a multi step problem. The experimental results show that (1) XCSB can solve quickly the problem which has a large state space and (2) XCSB can achieve a high performance with a small max population size.


Test Problem Good Action Benchmark Problem Large State Space Step Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Masaya Nakata
    • 1
  • Pier Luca Lanzi
    • 2
  • Keiki Takadama
    • 1
  1. 1.Department of InformaticsThe university of Electo-CommunicationsTokyoJapan
  2. 2.Dipartimento di Elettronica e InformazionePolitecnico di MilanoMilanoItaly

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