An evolutionary behavioral model for decision making

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@Article{Romero_2011,
  author =       "Oscar J. {Romero Lopez}",
  title =        "An evolutionary behavioral model for decision making",
  journal =      "Adaptive Behavior",
  year =         "2011",
  volume =       "19",
  number =       "6",
  pages =        "451--475",
  month =        dec,
  keywords =     "genetic algorithms, genetic programming, , gene
                 expression programming, Intelligent and autonomous
                 agents, adaptive behavior, automated planning, behavior
                 networks, evolutionary computation",
  editor =       "Mss Sarah Taylor",
  publisher =    "Sage Publications, Inc.",
  URL =          "http://cogprints.org/8015/",
  DOI =          "doi:10.1177/1059712311419680",
  abstract =     "For autonomous agents the problem of deciding what to
                 do next becomes increasingly complex when acting in
                 unpredictable and dynamic environments while pursuing
                 multiple and possibly conflicting goals. One of the
                 most relevant behaviour-based models that tries to deal
                 with this problem is the behaviour network model
                 proposed by Maes. This model proposes a set of
                 behaviors as purposive perception–action units that
                 are linked in a non-hierarchical network, and whose
                 behavior selection process is orchestrated by spreading
                 activation dynamics. In spite of being an adaptive
                 model (in the sense of self-regulating its own behavior
                 selection process), and despite the fact that several
                 extensions have been proposed in order to improve the
                 original model adaptability, there is not yet a robust
                 model that can self-modify adaptively both the
                 topological structure and the functional purpose of the
                 network as a result of the interaction between the
                 agent and its environment. Thus, this work proposes an
                 innovative hybrid model driven by gene expression
                 programming, which makes two main contributions: (1)
                 given an initial set of meaningless and unconnected
                 units, the evolutionary mechanism is able to build
                 well-defined and robust behavior networks that are
                 adapted and specialized to concrete internal agent's
                 needs and goals; and (2) the same evolutionary
                 mechanism is able to assemble quite complex structures
                 such as deliberative plans (which operate in the
                 long-term) and problem-solving strategies. As a result,
                 several properties of self-organization and
                 adaptability emerged when the proposed model was tested
                 in a robotic environment using a multi-agent
                 platform.",
  notes =        "Animals, Animats, Software Agents, Robots, Adaptive
                 Systems",
}

Genetic Programming entries for Oscar Javier Romero Lopez

Citations