Real Time Evolution of Behavior and a World Model for a Miniature Robot using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@TechReport{oai:CiteSeerPSU:392556,
  author =       "Peter Nordin and Wolfgang Banzhaf",
  title =        "Real Time Evolution of Behavior and a World Model for
                 a Miniature Robot using Genetic Programming",
  institution =  "Department of Computer SCience, University of
                 Dortmund",
  year =         "1995",
  type =         "Technical Report",
  number =       "SysReport 5/95",
  address =      "44221 Dormund, Germany",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://web.cs.mun.ca/~banzhaf/papers/mem_robot.ps.gz",
  URL =          "http://citeseer.ist.psu.edu/392556.html",
  citeseer-isreferencedby = "oai:CiteSeerPSU:53129;
                 oai:CiteSeerPSU:560715; oai:CiteSeerPSU:520099",
  annote =       "The Pennsylvania State University CiteSeer Archives",
  language =     "en",
  oai =          "oai:CiteSeerPSU:392556",
  rights =       "unrestricted",
  abstract =     "A very general form of representing and specifying an
                 autonomous agent's behavior is by using a computer
                 language. The task of planning feasible actions could
                 then simply be reduced to an instance of automatic
                 programming. We have evaluated the use of an
                 evolutionary technique for automatic programming called
                 Genetic Programming (GP) to directly control a
                 miniature robot. To our knowledge, this is the first
                 attempt to control a real robot with a GP based
                 learning method. Two schemes are presented. The
                 objective of the GP-system in our first approach is to
                 evolve real-time obstacle avoiding behavior from
                 sensorial data. This technique enables real time
                 learning with a real robot using genetic programming,
                 it has, however, the drawback of the learning time
                 being limited by the response dynamics of the
                 environment. To overcome this problems we have devised
                 a second method, learning from past experiences stored
                 in memory. This new system allows speeds up of the
                 algorithm by a factor or more than 2000...",
  size =         "32 pages",
}

Genetic Programming entries for Peter Nordin Wolfgang Banzhaf

Citations