Evolving a Multiagent Coordination Strategy Using Genetic Network Programming for Pursuit Domain

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

@InProceedings{Naeini:2008:cec,
  author =       "Armin Tavakoli Naeini and Maziar Palhang",
  title =        "Evolving a Multiagent Coordination Strategy Using
                 Genetic Network Programming for Pursuit Domain",
  booktitle =    "2008 IEEE World Congress on Computational
                 Intelligence",
  year =         "2008",
  editor =       "Jun Wang",
  pages =        "3102--3107",
  address =      "Hong Kong",
  month =        "1-6 " # jun,
  organization = "IEEE Computational Intelligence Society",
  publisher =    "IEEE Press",
  isbn13 =       "978-1-4244-1823-7",
  file =         "EC0686.pdf",
  DOI =          "doi:10.1109/CEC.2008.4631217",
  abstract =     "The design and development of strategies to coordinate
                 the actions of multiple agents is a central research
                 issue in the field of Multiagent Systems (MAS). It is
                 nearly impossible to identify or prove the existence of
                 the best coordination strategy. In most cases a
                 coordination strategy is chosen for a domain, if it is
                 reasonably good.In this paper, we propose a new design
                 methodology using Genetic Network Programming (GNP) to
                 evolve a coordination strategy for a well-known and
                 difficult-to-solve multi agent problem named pursuit
                 domain where cooperation of agents is required. Genetic
                 Network Programming (GNP) is a newly developed
                 evolutionary computation inspired from Genetic
                 Programming (GP). While GP uses a tree structure as
                 genes of an individual, GNP uses a directed graph type
                 structure. We show the effectiveness of proposed
                 methodology through simulations. In addition, the
                 comparison of the performances between GNP and GP is
                 carried out. The results show that performance of GNP
                 solution is significantly superior to GP solution.",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "WCCI 2008 - A joint meeting of the IEEE, the INNS, the
                 EPS and the IET.",
}

Genetic Programming entries for Armin Tavakoli Naeini Maziar Palhang

Citations