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XCSR with Computed Continuous Action

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AI 2012: Advances in Artificial Intelligence (AI 2012)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7691))

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Abstract

Wilson extended XCS with interval based conditions to XCSR to handle real valued inputs. However, the possible actions must always be determined in advance. Yet domains such as robot control require numerical actions, so that neither XCS nor XCSR with their discrete actions can yield high performance. In the work presented here, genetic programming-based representation is used for the first time to compute continuous action in XCSR. This XCSR version has been examined on a simple one-dimensional but non-linear testbed problem – the “frog” problem – and compared with two continuous action based systems, GCS and XCSFCA. The proposed approach has consistently solved the frog problem and outperformed GCS and XCSFCA.

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Author information

Authors and Affiliations

  1. Evolutionary Computation Research Group, School of Engineering and Computer Science, Victoria University of Wellington, PO Box 600, Wellington, 6140, New Zealand

    Muhammad Iqbal, Will N. Browne & Mengjie Zhang

Authors
  1. Muhammad Iqbal
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  2. Will N. Browne
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  3. Mengjie Zhang
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Editor information

Editors and Affiliations

  1. School of Computer Science and Engineering, University of New South Wales, 2052, Sydney, NSW, Australia

    Michael Thielscher

  2. School of Computing and Mathematics, University of Western Sydney, 1797, Penrith South DC, NSW, Australia

    Dongmo Zhang

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Iqbal, M., Browne, W.N., Zhang, M. (2012). XCSR with Computed Continuous Action. In: Thielscher, M., Zhang, D. (eds) AI 2012: Advances in Artificial Intelligence. AI 2012. Lecture Notes in Computer Science(), vol 7691. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35101-3_30

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  • DOI: https://doi.org/10.1007/978-3-642-35101-3_30

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-35100-6

  • Online ISBN: 978-3-642-35101-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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