A Study of Autonomous Agent Navigation Algorithms in the ANIMAT Environment

  • Arthur Pchelkin
  • Arkady Borisov
Conference paper


This paper deals with the examination of possibilities of the algorithm of autonomous agent navigation. The ANIMAT problem was developed to study the possibilities of ZCS classifier system learning. A ZCS classifier system is purely reactive. For this system it is essential to have a possibility to identify the global state of the environment by the input message. This reduces the application area of the system and its ability to solve complex tasks. The present paper proposes to build an alternative learning algorithm that would be able to cope with the above mentioned problems. The effectiveness of the suggested algorithm is tested via practical experiments. In the experiments performed the algorithm has demonstrated its essential superiority over a classifier system with temporary memory.


Optimal Policy Target State Classifier System Belief State Agent Learning 
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 London 2002

Authors and Affiliations

  • Arthur Pchelkin
    • 1
  • Arkady Borisov
    • 1
  1. 1.Riga Technical UniversityRigaLatvia

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