Multi-agent Learning of Heterogeneous Robots by Evolutionary Subsumption

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

@InProceedings{liu:2003:gecco,
  author =       "Hongwei Liu and Hitoshi Iba",
  title =        "Multi-agent Learning of Heterogeneous Robots by
                 Evolutionary Subsumption",
  booktitle =    "Genetic and Evolutionary Computation -- GECCO-2003",
  editor =       "E. Cant{\'u}-Paz and J. A. Foster and K. Deb and 
                 D. Davis and R. Roy and U.-M. O'Reilly and H.-G. Beyer and 
                 R. Standish and G. Kendall and S. Wilson and 
                 M. Harman and J. Wegener and D. Dasgupta and M. A. Potter and 
                 A. C. Schultz and K. Dowsland and N. Jonoska and 
                 J. Miller",
  year =         "2003",
  pages =        "1715--1728",
  address =      "Chicago",
  publisher_address = "Berlin",
  month =        "12-16 " # jul,
  volume =       "2724",
  series =       "LNCS",
  ISBN =         "3-540-40603-4",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.iba.k.u-tokyo.ac.jp/papers/2003/lhwGECCO2003.pdf",
  DOI =          "doi:10.1007/3-540-45110-2_64",
  abstract =     "Many multi-robot systems are heterogeneous cooperative
                 systems, systems consisting of different species of
                 robots cooperating with each other to achieve a common
                 goal. This paper presents the emergence of cooperative
                 behaviors of heterogeneous robots by means of GP. Since
                 directly using GP to generate a controller for complex
                 behaviors is inefficient and intractable, especially in
                 the domain of multi-robot systems, we propose an
                 approach called Evolutionary Subsumption, which applies
                 GP to subsumption architecture. We test our approach in
                 an {"}eye{"}-{"}hand{"} cooperation problem. By
                 comparing our approach with direct GP and artificial
                 neural network (ANN) approaches, our experimental
                 results show that ours is more efficient in emergence
                 of complex behaviors.",
  notes =        "GECCO-2003. A joint meeting of the twelfth
                 International Conference on Genetic Algorithms
                 (ICGA-2003) and the eighth Annual Genetic Programming
                 Conference (GP-2003)",
}

Genetic Programming entries for Hongwei Liu Hitoshi Iba

Citations