Evolution of Mapmaking Ability: Strategies for the evolution of learning, planning, and memory using genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{andre:maps,
  author =       "David Andre",
  title =        "Evolution of Mapmaking Ability: Strategies for the
                 evolution of learning, planning, and memory using
                 genetic programming",
  booktitle =    "Proceedings of the 1994 IEEE World Congress on
                 Computational Intelligence",
  year =         "1994",
  volume =       "1",
  pages =        "250--255",
  address =      "Orlando, Florida, USA",
  month =        "27-29 " # jun,
  publisher =    "IEEE Press",
  DOI =          "doi:10.1109/ICEC.1994.350007",
  keywords =     "genetic algorithms, genetic programming, evolved
                 representations, gold collection, information encoding,
                 intelligent agent, learning, mapmaking evolution;
                 memory, multi-phasic fitness environment, planning,
                 brain models, cartography, cognitive systems, learning
                 (artificial intelligence), planning (artificial
                 intelligence)",
  abstract =     "An essential component of an intelligent agent is the
                 ability to observe, encode, and use information about
                 its environment. Traditional approaches to genetic
                 programming have focused on evolving functional or
                 reactive programs with only a minimal use of state.
                 This paper presents an approach for investigating the
                 evolution of learning, planning, and memory using
                 genetic programming. The approach uses a multi-phasic
                 fitness environment that enforces the use of memory and
                 allows fairly straightforward comprehension of the
                 evolved representations. An illustrative problem of
                 `gold' collection is used to demonstrate the usefulness
                 of the approach. The results indicate that the approach
                 can evolve programs that store simple representations
                 of their environments and use these representations to
                 produce simple plans",
}

Genetic Programming entries for David Andre

Citations