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:1.7630

@InProceedings{bennett:1996:emaant,

• 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",
• DOI = "doi:10.7551/mitpress/3118.003.0044",
• DOI = "doi:10.7551/mitpress/3118.001.0001",
• 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

Citations