Learning Complex Robot Behaviours by Evolutionary Computing with Task Decomposition

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  author =       "Wei-Po Lee and John Hallam and Henrik Hautop Lund",
  title =        "Learning Complex Robot Behaviours by Evolutionary
                 Computing with Task Decomposition",
  booktitle =    "Learning Robots, 6th European Workshop, EWLR-6,
  year =         "1997",
  editor =       "Andreas Birk and John Demiris",
  series =       "LNAI",
  volume =       "1545",
  pages =        "155--172",
  address =      "Hotel Metropole, Brighton, UK",
  month =        "1-2 " # aug,
  publisher =    "Springer Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-65480-1",
  DOI =          "doi:10.1007/3-540-49240-2_11",
  size =         "10 pages",
  abstract =     "Building robots can be a tough job because the
                 designer has to predict the interactions between the
                 robot and the environment as well as to deal with them.
                 One solution to cope the difficulties in designing
                 robots is to adopt learning methods. Evolution-based
                 approaches are a special kind of machine learning
                 method and during the last few years some researchers
                 have shown the advantages of using this kind of
                 approach to automate the design of robots. However, the
                 tasks achieved so far are fairly simple. In this work,
                 we analyse the difficulties of applying evolutionary
                 approaches to learn complex behaviours for mobile
                 robots. And, instead of evolving the controller as a
                 whole, we propose to take the control architecture of a
                 behavior-based system and to learn the separate
                 behaviours and the arbitration by the use of an
                 evolutionary approach. By using the technique of task
                 decomposition, the job of defining fitness functions
                 becomes more straightforward and the tasks become
                 easier to achieve. To assess the performance of the
                 developed approach, we have evolved a control system to
                 achieve an application task of box-pushing as an
                 example. Experimental results show the promise and
                 efficiency of the presented approach.",
  notes =        "Published version may be different from that in
                 proceedings \cite{lee:1997:lcrbea}",

Genetic Programming entries for Wei-Po Lee John Hallam Henrik Hautop Lund