Graduated Embodiment for Sophisticated Agent Evolution and Optimization
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @TechReport{SAND2005-0014,
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author = "Mark Boslough and Michael Peters and
Arthurine Pierson",
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title = "Graduated Embodiment for Sophisticated Agent Evolution
and Optimization",
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institution = "Sandia National Laboratories",
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year = "2005",
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number = "SAND2005-0014",
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address = "P.O. Box 5800, Albuquerque, NM 87185-0318, USA",
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month = jan,
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keywords = "genetic algorithms, genetic programming, Algorithms
Design, Evaluation, Genetics, Hypothesis, Navigation,
Optimization, Programming Robots, Simulation, Training,
Intelligent agents (Computer software), Robots-Control
systems, Evolutionary programming (Computer science)",
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URL = "https://www.sandia.gov/research/publications/details/graduated-embodiment-for-sophisticated-agent-evolution-and-optimization-2005-01-01/",
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URL = "http://www.cs.sandia.gov/web1433/pubsagent/Graduated_Embodiment.pdf",
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DOI = "doi:10.2172/921610",
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size = "53 pages",
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abstract = "We summarise the results of a project to develop
evolutionary computing methods for the design of
behaviours of embodied agents in the form of autonomous
vehicles. We conceived and implemented a strategy
called graduated embodiment. This method allows
high-level behavior algorithms to be developed using
genetic programming methods in a low-fidelity,
disembodied modelling environment for migration to
high-fidelity, complex embodied applications. This
project applies our methods to the problem domain of
robot navigation using adaptive waypoints, which allow
navigation behaviors to be ported among autonomous
mobile robots with different degrees of embodiment,
using incremental adaptation and staged optimisation.
Our approach to biomimetic behaviour engineering is a
hybrid of human design and artificial evolution, with
the application of evolutionary computing in stages to
preserve building blocks and limit search space. The
methods and tools developed for this project are
directly applicable to other agent-based modeling
needs, including climate-related conflict analysis,
multiplayer training methods,and market-based
hypothesis evaluation.",
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notes = "Unlimited Release
Mark Boslough Michael Peters Evolutionary Computing &
Agent Based Modeling Department
Arthurine Pierson Intelligent Systems Principles
Department",
- }
Genetic Programming entries for
Mark Boslough
Michael D Peters
Arthurine Pierson
Citations