Animats: computer-simulated animals in behavioral research

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

@Article{watts:1998:jas,
  author =       "Jon M. Watts",
  title =        "Animats: computer-simulated animals in behavioral
                 research",
  journal =      "Journal of Animal Science",
  year =         "1998",
  volume =       "76",
  number =       "10",
  pages =        "2596--2604",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://jas.fass.org/cgi/reprint/76/10/2596.pdf",
  URL =          "http://jas.fass.org/cgi/reprint/76/10/2596",
  size =         "9 pages",
  abstract =     "The term animat refers to a class of simulated
                 animals. This article is intended as a nontechnical
                 introduction to animat research. Animats can be robots
                 interacting with the real world or computer
                 simulations. In this article, the use of
                 computer-generated animats is emphasised. The
                 scientific use of animats has been pioneered by
                 artificial intelligence and artificial life
                 researchers. Behaviour-based artificial intelligence
                 uses animats capable of autonomous and adaptive
                 activity as conceptual tools in the design of usefully
                 intelligent systems. Artificial life proponents view
                 some human artifacts, including informational
                 structures that show adaptive behavior and
                 self-replication, as animats may do, as analogous to
                 biological organisms. Animat simulations may be used
                 for rapid and inexpensive evaluation of new livestock
                 environments or management techniques. The animat
                 approach is a powerful heuristic for understanding the
                 mechanisms that underlie behavior. The simple rules and
                 capabilities of animat models generate emergent and
                 sometimes unpredictable behavior. Adaptive variability
                 in animat behavior may be exploited using artificial
                 neural networks. These have computational properties
                 similar to natural neurons and are capable of learning.
                 Artificial neural networks can control behavior at all
                 levels of an animat's functional organization.
                 Improving the performance of animats often requires
                 genetic programming. Genetic algorithms are computer
                 programs that are capable of self-replication,
                 simulating biological reproduction. Animats may thus
                 evolve over generations. Selective forces may be
                 provided by a human overseer or be part of the
                 simulated environment. Animat techniques allow
                 researchers to culture behavior outside the organism
                 that usually produces it. This approach could
                 contribute new insights in theoretical ethology on
                 questions including the origins of social behavior and
                 cooperation, adaptation, and the emergent nature of
                 complex behavior. Animat studies applied to domestic
                 animals have been few so far, and have involved
                 simulations of space use by swine. I suggest other
                 applications, including modeling animal movement during
                 human handling and the effects of environmental
                 enrichment on the satisfaction of behavioral needs.
                 Appropriate use of animat models in a research program
                 could result in savings of time and numbers of animals
                 required. This approach may therefore come to be viewed
                 as both ethically and economically advantageous.",
  notes =        "Department of Herd Medicine and Theriogenology,
                 Western College of Veterinary Medicine, University of
                 Saskatchewan, Saskatoon, Canada.

                 PMID: 9814899 [PubMed - indexed for MEDLINE]",
}

Genetic Programming entries for Jon M Watts

Citations