Evolving complex robot behaviors

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{Wei-PoLee:1999:ISJ,
  author =       "Wei-Po Lee",
  title =        "Evolving complex robot behaviors",
  journal =      "Information Sciences",
  year =         "1999",
  volume =       "121",
  number =       "1-2",
  pages =        "1--25",
  email =        "wplee@mail.npust.edu.tw",
  keywords =     "genetic algorithms, genetic programming, Evolutionary
                 computing, Computational intelligence, Robot learning,
                 Automatic robot programming",
  ISSN =         "0020-0255",
  ISSN =         "0020-0255",
  DOI =          "doi:10.1016/S0020-0255(99)00078-X",
  URL =          "http://www.sciencedirect.com/science/article/B6V0C-3Y3XPFF-1/2/ddd56f7f1c35319bee24c38eb8db5652",
  abstract =     "Building robots is 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 such difficulties in designing robots is to
                 adopt learning methods. The evolution-based approach is
                 a special method of machine learning and it has been
                 advocated to automate the design of robots. Yet, the
                 tasks achieved so far are fairly simple. In this work,
                 we first analyze the difficulties of applying
                 evolutionary approaches to synthesize robot controllers
                 for complicated tasks, and then suggest an approach to
                 resolve them. Instead of directly evolving a monolithic
                 control system, we propose to decompose the overall
                 task to fit in the behavior-based control architecture,
                 and then to evolve the separate behavior modules and
                 arbitrators using an evolutionary approach.
                 Consequently, the job of defining fitness functions
                 becomes more straightforward and the tasks easier to
                 achieve. To assess the performance of the developed
                 approach, we evolve 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 =        "Khepera Information Sciences
                 http://www.elsevier.com/inca/publications/store/5/0/5/7/3/0/505730.pub.htt",
}

Genetic Programming entries for Wei-Po Lee

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