Multi-agent Learning of Heterogeneous Robots by Evolutionary Subsumption
Created by W.Langdon from
gp-bibliography.bib Revision:1.8010
- @InProceedings{liu:2003:gecco,
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author = "Hongwei Liu and Hitoshi Iba",
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title = "Multi-agent Learning of Heterogeneous Robots by
Evolutionary Subsumption",
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booktitle = "Genetic and Evolutionary Computation -- GECCO-2003",
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editor = "E. Cant{\'u}-Paz and J. A. Foster and K. Deb and
D. Davis and R. Roy and U.-M. O'Reilly and H.-G. Beyer and
R. Standish and G. Kendall and S. Wilson and
M. Harman and J. Wegener and D. Dasgupta and M. A. Potter and
A. C. Schultz and K. Dowsland and N. Jonoska and
J. Miller",
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year = "2003",
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pages = "1715--1728",
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address = "Chicago",
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publisher_address = "Berlin",
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month = "12-16 " # jul,
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volume = "2724",
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series = "LNCS",
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ISBN = "3-540-40603-4",
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publisher = "Springer-Verlag",
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keywords = "genetic algorithms, genetic programming",
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URL = "http://www.iba.k.u-tokyo.ac.jp/papers/2003/lhwGECCO2003.pdf",
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DOI = "doi:10.1007/3-540-45110-2_64",
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abstract = "Many multi-robot systems are heterogeneous cooperative
systems, systems consisting of different species of
robots cooperating with each other to achieve a common
goal. This paper presents the emergence of cooperative
behaviors of heterogeneous robots by means of GP. Since
directly using GP to generate a controller for complex
behaviors is inefficient and intractable, especially in
the domain of multi-robot systems, we propose an
approach called Evolutionary Subsumption, which applies
GP to subsumption architecture. We test our approach in
an {"}eye{"}-{"}hand{"} cooperation problem. By
comparing our approach with direct GP and artificial
neural network (ANN) approaches, our experimental
results show that ours is more efficient in emergence
of complex behaviors.",
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notes = "GECCO-2003. A joint meeting of the twelfth
International Conference on Genetic Algorithms
(ICGA-2003) and the eighth Annual Genetic Programming
Conference (GP-2003)",
- }
Genetic Programming entries for
Hongwei Liu
Hitoshi Iba
Citations