Evolving Virtual Agents using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4340

  author =       "Patrick Monsieurs",
  title =        "Evolving Virtual Agents using Genetic Programming",
  school =       "Limburg University",
  year =         "2002",
  address =      "Diepenbeek, Belgium",
  month =        "5 " # dec,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://hdl.handle.net/1942/8865",
  broken =       "http://alpha.luc.ac.be/~lucp1089/Doctoraatsthesis.pdf",
  broken =       "http://www.edm.uhasselt.be/publications/show/148",
  URL =          "http://tech.groups.yahoo.com/group/genetic_programming/message/1270",
  size =         "179 pages",
  abstract =     "Virtual environments are used in a diverse number of
                 applications, ranging from medical applications,
                 military simulations, modelling and engineering, to
                 entertainment such as games or virtual communities. In
                 these applications, virtual agents can be used to make
                 the environment more realistic, perform tasks that are
                 tedious and time consuming for humans, or even simulate
                 the presence of other users in the environment.

                 When constructing an agent for a virtual environment,
                 several issues are encountered that must be resolved.
                 First, a virtual agent must be able to explore and
                 navigate in the virtual environment in a realistic way
                 while avoiding collisions with obstacles. If the
                 virtual agent does not have access to the internal
                 representation of the environment, it will have to use
                 its virtual sensors to observe the environment. In this
                 thesis, an algorithm is presented to perform obstacle
                 avoidance and map construction in a virtual environment
                 using a synthetic vision sensor. The constructed map
                 can then also be used to navigate in the environment.

                 A second issue is communication between agents and
                 users in the environment. Agents and users must be able
                 to locate agents that can perform certain tasks, and
                 agents may offer their services to users or other
                 agents. These issues are discussed briefly in this
                 thesis, and a prototype of a multi-agent virtual
                 environment is presented.

                 The most difficult issue of virtual agents is learning
                 to solve problems in an environment, without knowing
                 the constraints and rules of the environment in
                 advance. This thesis will examine the use of genetic
                 programming to train virtual agents. Two important
                 problems are encountered when using genetic programming
                 in this domain. First, programs constructed using
                 genetic programming tend to grow rapidly before an
                 acceptable solution is found. Several techniques will
                 be presented to reduce the size of the evolved genetic
                 programs, and a comparison will be made between these
                 techniques. Secondly, evaluation of candidate solutions
                 is usually very time consuming, making it impractical
                 to maintain a large population of candidate solution. A
                 large population is usually a requirement to evolve
                 good solutions. Therefore, an algorithm to reduce the
                 size of the population while maintaining the diversity
                 of a larger population is presented. These
                 optimisations will also be applied to the virtual
                 multi-agent system of robotic soccer to examine the
                 effects of these optimizations in a complex
  notes =        "Summary in Flemish Nederlaans Robocup, parity, stgp,
                 bloat, santafe ant, map construction, synthetic

                 Advisors: Eddy Flerackers and F. {VAN REETH}",

Genetic Programming entries for Patrick Monsieurs