The Evolution of Autonomous Agents Using Concurrent Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Adrian Trenaman",
  title =        "The Evolution of Autonomous Agents Using Concurrent
                 Genetic Programming",
  school =       "Department of Computer Science, National University of
                 Ireland, Maynooth",
  year =         "1999",
  address =      "Ireland",
  month =        oct,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "",
  size =         "136 pages",
  abstract =     "This thesis addresses the issue of how computational
                 agents interact with and represent their environment in
                 order to effect goal-achieving behaviour. It argues
                 that the internal representations used by the agent to
                 describe objects in the world should be based on how
                 the agent perceives these objects and not necessarily
                 on the representations a human designer might impose. A
                 bottom-up methodology is proposed for the automatic
                 design of distributed algorithms and internal
                 representations to control autonomous agents. In
                 particular, this thesis proposes and evaluates a new
                 mechanism for the evolution of agents: {"}concurrent
                 genetic programming''. In this encoding scheme an agent
                 is controlled by a set of evolved programs that are
                 executed concurrently to yield an emergent control
                 algorithm for the agent. This encoding forms a natural
                 interpretation of the emergent principles of the
                 discipline of artificial life in an evolutionary
                 context, and so elucidates the ability of evolutionary
                 computation to create such emergent systems. The
                 performance of the approach is investigated as a
                 function of several parameters. These are: using
                 different numbers of programs in the agents, explicit
                 memory, distributed memory architectures, deterministic
                 and non-deterministic scheduling strategies, different
                 levels of granularity of concurrency, and the evolution
                 of scheduling strategy. These issues are investigated
                 through the application of concurrent genetic
                 programming to the standard Tartarus and Dozer
                 virtual-robotics benchmarks. It is shown that
                 concurrent genetic programming produces better agents
                 for these environments than a conventional genetic
                 programming approach. It does this by employing an
                 implicit form of state that supports the development of
                 cyclical behaviour strategies. Implicit representations
                 of the environment are acquired at an evolutionary
                 level rather than at the level of the agent's
                 experience. Although this form of internal
                 representation leads to fit agents, it does not exhibit
                 the formation of explicit models of the agent's
                 environment. Instead, it allows the development of a
                 form of internal state appropriate to achieving good
  notes =        "


Genetic Programming entries for Adrian Trenaman