Agent-Based Evolutionary Model for Knowledge Acquisition in Dynamical Environments

  • Wojciech Froelich
  • Marek Kisiel-Dorohinicki
  • Edward Nawarecki
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3993)


The basic idea of the approach proposed in this paper is to apply multi-agent paradigm in order to enable the integration and co-operation of different knowledge acquisition and representation techniques. The effective operation of learning process is achieved by evolutionary optimization running at the level of agents’ population. In the discussed variant of the model, each agent uses reinforcement learning, and the obtained knowledge is represented as the set of simple decision rules. The approach is illustrated by a particular realization of the system dedicated to the evasive maneuvers problem, together with preliminary experimental results.


Multiagent System Knowledge Acquisition Observation Vector Decision Vector Iconic Memory 
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 2006

Authors and Affiliations

  • Wojciech Froelich
    • 1
  • Marek Kisiel-Dorohinicki
    • 2
  • Edward Nawarecki
    • 2
  1. 1.Institute of Computer ScienceSilesian UniversitySosnowiecPoland
  2. 2.Institute of Computer ScienceAGH University of Science and TechnologyKrakówPoland

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