A study of evolutionary multiagent models based on symbiosis

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Toru Eguchi and Kotaro Hirasawa and Jinglu Hu and 
                 Nathan Ota",
  title =        "A study of evolutionary multiagent models based on
  journal =      "IEEE Transactions on Systems, Man, and Cybernetics,
                 Part B",
  volume =       "36",
  number =       "1",
  year =         "2006",
  pages =        "179--193",
  month =        feb,
  keywords =     "genetic algorithms, genetic programming, decision
                 making, evolutionary computation, graph theory,
                 learning (artificial intelligence), multi-agent
                 systems, directed graph, evolutionary multiagent
                 models, genetic network programming, match type
                 tile-world, nash equilibria, symbiosis multiagent
                 systems, symbiotic evolution, symbiotic learning,
                 virtual model, Evolutionary computation, multiagent
                 systems, symbiosis, tile-world",
  bibsource =    "DBLP, http://dblp.uni-trier.de",
  DOI =          "doi:10.1109/TSMCB.2005.856720",
  size =         "15 pages",
  abstract =     "Multiagent Systems with Symbiotic Learning and
                 Evolution (Masbiole) has been proposed and studied,
                 which is a new methodology of Multiagent Systems (MAS)
                 based on symbiosis in the ecosystem. Masbiole employs a
                 method of symbiotic learning and evolution where agents
                 can learn or evolve according to their symbiotic
                 relations toward others, i.e., considering the
                 benefits/losses of both itself and an opponent. As a
                 result, Masbiole can escape from Nash Equilibria and
                 obtain better performances than conventional MAS where
                 agents consider only their own benefits. This paper
                 focuses on the evolutionary model of Masbiole, and its
                 characteristics are examined especially with an
                 emphasis on the behaviours of agents obtained by
                 symbiotic evolution. In the simulations, two ideas
                 suitable for the effective analysis of such behaviors
                 are introduced; {"}Match Type Tile-world (MTT){"} and
                 {"}Genetic Network Programming (GNP){"}. MTT is a
                 virtual model where tile-world is improved so that
                 agents can behave considering their symbiotic
                 relations. GNP is a newly developed evolutionary
                 computation which has the directed graph type gene
                 structure and enables to analyse the decision making
                 mechanism of agents easily. Simulation results show
                 that Masbiole can obtain various kinds of behaviours
                 and better performances than conventional MAS in MTT by

Genetic Programming entries for Toru Eguchi Kotaro Hirasawa Jinglu Hu Nathan Ota