Emergence of a Multi-Agent Architecture and New Tactics For the Ant Colony Foraging Problem Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  author =       "Forrest H {Bennett III}",
  title =        "Emergence of a Multi-Agent Architecture and New
                 Tactics For the Ant Colony Foraging Problem Using
                 Genetic Programming",
  booktitle =    "Proceedings of the Fourth International Conference on
                 Simulation of Adaptive Behavior: From animals to
                 animats 4",
  year =         "1996",
  editor =       "Pattie Maes and Maja J. Mataric and 
                 Jean-Arcady Meyer and Jordan Pollack and Stewart W. Wilson",
  pages =        "430--439",
  address =      "Cape Code, USA",
  publisher_address = "Cambridge, MA, USA",
  month =        "9-13 " # sep,
  publisher =    "MIT Press",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-262-63178-4",
  URL =          "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6291906",
  size =         "10 pages",
  abstract =     "Previous work in multi-agent systems has required the
                 human designer to make up-front decisions about the
                 multi-agent architecture, including the number of
                 agents to employ and the specific tasks to be performed
                 by each agent. This paper describes the automatic
                 evolution of these decisions during a run of genetic
                 programming using architecture-altering
                 operations.Genetic programming is extended to the
                 discovery of multi-agent solutions for a central-place
                 foraging problem for an ant colony. In this problem
                 each individual ant is controlled by a set of agents,
                 where agent is used in the sense of Minsky's Society of
                 Mind.Two new tactics for the central-place food
                 foraging problem that were discovered by genetic
                 programming are presented in this paper.Genetic
                 programming was able to evolve time-efficient solutions
                 to this problem by distributing the functions and
                 terminals across successively more agents in such a way
                 as to reduce the maximum number of functions executed
                 per agent. The other source of time-efficiency in the
                 evolved solution was the cooperation that emerged among
                 the ants in the ant colony.",
  notes =        "SAB-96 Each tree within individual treated as an
                 {"}agent{"}. Uses koza add/delete adf genetic
                 operations to evolve the number of agents as well as
                 their code.",

Genetic Programming entries for Forrest Bennett