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Using action abstraction to evolve effective controllers

Published: 08 July 2009 Publication History

Abstract

We propose that abstracting the actions of a behavior coordination mechanism promotes the faster development and higher fitness of an effective controller for complex, composite tasks. Various techniques are well suited for the development of controllers for individual simple tasks. However, as individual tasks are combined into complex, composite tasks, many of these techniques quickly become impractical. By reusing existing behaviors, the focus of development for a controller can be shifted from low-level control to high-level coordination of these existing behaviors. As a result, the development of an effective controller becomes far more practical. Experiments using a single-agent task in a continuous environment demonstrate that grammatical evolution is capable of discovering fuzzy rulesets which effectively coordinate existing behaviors in a controller in fewer generations and with higher fitness than monolithic controllers.

References

[1]
M. O'Neill and C. Ryan. Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language. Kluwer Academic Publishers, Norwell, MA, USA, 2003.
[2]
E. Tunstel. Fuzzy-behavior synthesis, coordination, and evolution in an adaptive behavior hierarchy. In Fuzzy Logic Techniques for Autonomous volume 61 of Studies in Fuzziness and Soft Computing, chapter 9, pages 205--234. Springer-Phisica Verlag, 2001.

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    cover image ACM Conferences
    GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation
    July 2009
    2036 pages
    ISBN:9781605583259
    DOI:10.1145/1569901

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    Association for Computing Machinery

    New York, NY, United States

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    Published: 08 July 2009

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    Author Tags

    1. artificial intelligence
    2. evolutionary robotics
    3. fuzzy systems
    4. modelling behaviours and ecosystems

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    GECCO09
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    GECCO09: Genetic and Evolutionary Computation Conference
    July 8 - 12, 2009
    Québec, Montreal, Canada

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    Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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