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:1.7954
- @InProceedings{bennett:1996:emaant,
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author = "Forrest H {Bennett III}",
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title = "Emergence of a Multi-Agent Architecture and New
Tactics For the Ant Colony Foraging Problem Using
Genetic Programming",
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booktitle = "Proceedings of the Fourth International Conference on
Simulation of Adaptive Behavior: From animals to
animats 4",
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year = "1996",
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editor = "Pattie Maes and Maja J. Mataric and
Jean-Arcady Meyer and Jordan Pollack and Stewart W. Wilson",
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pages = "430--439",
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address = "Cape Code, USA",
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publisher_address = "Cambridge, MA, USA",
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month = "9-13 " # sep,
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publisher = "MIT Press",
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keywords = "genetic algorithms, genetic programming",
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ISBN = "0-262-63178-4",
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URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6291906",
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DOI = "doi:10.7551/mitpress/3118.003.0044",
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DOI = "doi:10.7551/mitpress/3118.001.0001",
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size = "10 pages",
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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.",
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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