Improving Performance and Cooperation in Multi-Agent Systems

Created by W.Langdon from gp-bibliography.bib Revision:1.4221

  author =       "Terence Soule and Robert B. Heckendorn",
  title =        "Improving Performance and Cooperation in Multi-Agent
  booktitle =    "Genetic Programming Theory and Practice {V}",
  year =         "2007",
  editor =       "Rick L. Riolo and Terence Soule and Bill Worzel",
  series =       "Genetic and Evolutionary Computation",
  chapter =      "13",
  pages =        "223--240",
  address =      "Ann Arbor",
  month =        "17-19" # may,
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-0-387-76308-8",
  DOI =          "doi:10.1007/978-0-387-76308-8_13",
  size =         "17 pages",
  abstract =     "Research has shown that evolutionary algorithms are a
                 promising approach for training agents in heterogeneous
                 multi-agent systems. However, research in evolving
                 teams (or ensembles) has proven that common
                 evolutionary approaches have subtle, but significant,
                 weaknesses when it comes to balancing member
                 performance and member cooperation. In addition, there
                 are potentially significant scaling problems in
                 applying evolutionary techniques to very large
                 multi-agent systems. It is impractical to train each
                 member of a large system individually, but purely
                 homogeneous teams are inadequate. Previously we
                 proposed Orthogonal Evolution of Teams (OET) as a novel
                 approach to evolving teams that overcomes the
                 weaknesses with balancing member performance and member
                 cooperation. In this paper we test two basic
                 evolutionary techniques and OET on the problem of
                 evolving multi-agent systems, specifically a landscape
                 exploration problem with heterogeneous agents, and
                 examine the ability of the algorithms to evolve teams
                 that are scalable in the number of team members. Our
                 results confirm that the more traditional evolutionary
                 approaches suffer the same weakness with multi-agent
                 systems as they do with teams and that OET does
                 compensate for these weaknesses. In addition, the three
                 algorithms show distinctly different scaling behaviour,
                 with OET scaling significantly better than the two more
                 traditional approaches.",
  notes =        "part of \cite{Riolo:2007:GPTP} published 2008",

Genetic Programming entries for Terence Soule Robert B Heckendorn