Graduated Embodiment for Sophisticated Agent Evolution and Optimization

Created by W.Langdon from gp-bibliography.bib Revision:1.4504

  author =       "Mark Boslough and Michael Peters and 
                 Arthurine Pierson",
  title =        "Graduated Embodiment for Sophisticated Agent Evolution
                 and Optimization",
  institution =  "Sandia National Laboratories",
  year =         "2005",
  number =       "SAND2005-0014",
  address =      "P.O. Box 5800, Albuquerque, NM 87185-0318, USA",
  month =        jan,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "",
  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.",
  notes =        "Unlimited Release

                 Mark Boslough Michael Peters Evolutionary Computing &
                 Agent Based Modeling Department

                 Arthurine Pierson Intelligent Systems Principles
  size =         "53 pages",

Genetic Programming entries for Mark Boslough Michael D Peters Arthurine Pierson