Learning Complex Robot Behaviours by Evolutionary Computing with Task Decomposition
Created by W.Langdon from
gp-bibliography.bib Revision:1.7975
- @InProceedings{lee:1997:lcrbeaLNAI,
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author = "Wei-Po Lee and John Hallam and Henrik Hautop Lund",
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title = "Learning Complex Robot Behaviours by Evolutionary
Computing with Task Decomposition",
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booktitle = "Learning Robots, 6th European Workshop, EWLR-6,
Proceedings",
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year = "1997",
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editor = "Andreas Birk and John Demiris",
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series = "LNAI",
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volume = "1545",
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pages = "155--172",
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address = "Hotel Metropole, Brighton, UK",
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month = "1-2 " # aug,
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publisher = "Springer Verlag",
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keywords = "genetic algorithms, genetic programming",
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ISBN = "3-540-65480-1",
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DOI = "doi:10.1007/3-540-49240-2_11",
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size = "10 pages",
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abstract = "Building robots can be a tough job because the
designer has to predict the interactions between the
robot and the environment as well as to deal with them.
One solution to cope the difficulties in designing
robots is to adopt learning methods. Evolution-based
approaches are a special kind of machine learning
method and during the last few years some researchers
have shown the advantages of using this kind of
approach to automate the design of robots. However, the
tasks achieved so far are fairly simple. In this work,
we analyse the difficulties of applying evolutionary
approaches to learn complex behaviours for mobile
robots. And, instead of evolving the controller as a
whole, we propose to take the control architecture of a
behavior-based system and to learn the separate
behaviours and the arbitration by the use of an
evolutionary approach. By using the technique of task
decomposition, the job of defining fitness functions
becomes more straightforward and the tasks become
easier to achieve. To assess the performance of the
developed approach, we have evolved a control system to
achieve an application task of box-pushing as an
example. Experimental results show the promise and
efficiency of the presented approach.",
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notes = "Published version may be different from that in
proceedings \cite{lee:1997:lcrbea}",
- }
Genetic Programming entries for
Wei-Po Lee
John Hallam
Henrik Hautop Lund
Citations