Guiding Evolutionary Learning by Searching for Regularities in Behavioral Trajectories: A Case for Representation Agnosticism
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{KrawiecSwanAAAI2013,
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author = "Krzysztof Krawiec and Jerry Swan",
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title = "Guiding Evolutionary Learning by Searching for
Regularities in Behavioral Trajectories: A Case for
Representation Agnosticism",
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booktitle = "How Should Intelligence Be Abstracted in AI Research:
{MDPs}, Symbolic Representations, Artificial Neural
Networks, or ...",
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year = "2013",
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editor = "Sebastian Risi and Joel Lehman and Jeff Clune",
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number = "FS-13-02",
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series = "2013 AAAI Fall Symposium Series",
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pages = "41--46",
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address = "Arlington, Virginia, USA",
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month = "15-17 " # nov,
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organisation = "Association for the Advancement of Artificial
Intelligence",
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publisher = "AAAI Press",
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publisher_address = "Menlo Park, California, USA",
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keywords = "genetic algorithms, genetic programming, pattern
guided GP",
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isbn13 = "978-1-57735-612-7",
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URL = "http://www.aaai.org/ocs/index.php/FSS/FSS13/paper/view/7590",
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URL = "http://www.aaai.org/ocs/index.php/FSS/FSS13/paper/view/7590/7506.pdf",
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size = "6 pages",
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abstract = "An intelligent agent can display behaviour that is not
directly related to the task it learns. Depending on
the adopted AI framework and task formulation, such
behaviour is sometimes attributed to environment
exploration, or ignored as irrelevant, or even
penalised as undesired. We postulate here that
virtually every interaction of an agent with its
learning environment can result in outcomes that carry
information which can be potentially exploited to solve
the task. To support this claim, we present Pattern
Guided Evolutionary Algorithm (PANGEA), an extension of
genetic programming (GP), a genre of evolutionary
computation that aims at synthesising programs that
display the desired input-output behavior. PANGEA uses
machine learning to search for regularities in
intermediate outcomes of program execution (which are
ignored in standard GP), more specifically for
relationships between these outcomes and the desired
program output. The information elicited in this way is
used to guide the evolutionary learning process by
appropriately adjusting program fitness. An experiment
conducted on a suite of benchmarks demonstrates that
this architecture makes agent learning more effective
than in conventional GP. In the paper, we discuss the
possible generalisations and extensions of this
architecture and its relationships with other
contemporary paradigms like novelty search and deep
learning. In conclusion, we extrapolate PANGEA to
postulate a dynamic and behavioural learning framework
for intelligent agents.",
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notes = "http://www.cs.ucf.edu/~risi/AAAISymposium2013/#program",
- }
Genetic Programming entries for
Krzysztof Krawiec
Jerry Swan
Citations