The Evolution of Autonomous Agents Using Concurrent Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @PhdThesis{trenaman:thesis,
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author = "Adrian Trenaman",
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title = "The Evolution of Autonomous Agents Using Concurrent
Genetic Programming",
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school = "Department of Computer Science, National University of
Ireland, Maynooth",
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year = "1999",
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address = "Ireland",
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month = oct,
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keywords = "genetic algorithms, genetic programming",
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URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/trenaman/at_thesis1.ps.gz",
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size = "136 pages",
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abstract = "This thesis addresses the issue of how computational
agents interact with and represent their environment in
order to effect goal-achieving behaviour. It argues
that the internal representations used by the agent to
describe objects in the world should be based on how
the agent perceives these objects and not necessarily
on the representations a human designer might impose. A
bottom-up methodology is proposed for the automatic
design of distributed algorithms and internal
representations to control autonomous agents. In
particular, this thesis proposes and evaluates a new
mechanism for the evolution of agents: {"}concurrent
genetic programming''. In this encoding scheme an agent
is controlled by a set of evolved programs that are
executed concurrently to yield an emergent control
algorithm for the agent. This encoding forms a natural
interpretation of the emergent principles of the
discipline of artificial life in an evolutionary
context, and so elucidates the ability of evolutionary
computation to create such emergent systems. The
performance of the approach is investigated as a
function of several parameters. These are: using
different numbers of programs in the agents, explicit
memory, distributed memory architectures, deterministic
and non-deterministic scheduling strategies, different
levels of granularity of concurrency, and the evolution
of scheduling strategy. These issues are investigated
through the application of concurrent genetic
programming to the standard Tartarus and Dozer
virtual-robotics benchmarks. It is shown that
concurrent genetic programming produces better agents
for these environments than a conventional genetic
programming approach. It does this by employing an
implicit form of state that supports the development of
cyclical behaviour strategies. Implicit representations
of the environment are acquired at an evolutionary
level rather than at the level of the agent's
experience. Although this form of internal
representation leads to fit agents, it does not exhibit
the formation of explicit models of the agent's
environment. Instead, it allows the development of a
form of internal state appropriate to achieving good
fitness.",
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notes = "
",
- }
Genetic Programming entries for
Adrian Trenaman
Citations