Evolutionary Computational Intelligence for Multi-Agent Simulations

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

@PhdThesis{Telmo_Menezes_PhD_Thesis,
  author =       "Telmo {de Lucena Torres de Menezes}",
  title =        "Evolutionary Computational Intelligence for
                 Multi-Agent Simulations",
  school =       "Universidade de Coimbra, Faculdade de Ciencias e
                 Tecnologia, Departamento de Engenharia Informatica",
  year =         "2008",
  address =      "Portugal",
  month =        dec,
  keywords =     "genetic algorithms, genetic programming, Computational
                 Intelligence, Multi-Agent Simulations, Evolutionary
                 Computation, Complex Systems, Artificial Life,
                 Emergence of Group-Behaviour, Bloat Control",
  URL =          "http://telmomenezes.com/resources/Telmo_Menezes_PhD_Thesis.pdf",
  size =         "294 pages",
  abstract =     "The growing interest in multi-agent simulations,
                 influenced by the advances in fields like the sciences
                 of complexity and artificial life is related to a
                 modern direction in computational intelligence
                 research. Instead of building isolated artificial
                 intelligence systems from the top-down, this new
                 approach attempts to design systems where a population
                 of agents and the environment interact and adaptation
                 processes take place.

                 We present a novel evolutionary platform to tackle the
                 problem of evolving computational intelligence in
                 multi-agent simulations. It consists of an artificial
                 brain model, called the gridbrain, a simulation
                 embedded evolutionary algorithm (SEEA) and a software
                 tool, LabLOVE.

                 The gridbrain model defines agent brains as
                 heterogeneous networks of computational building
                 blocks. A multi-layer approach allows gridbrains to
                 process variable-sized information from several sensory
                 channels. Computational building blocks allow for the
                 use of base functionalities close to the underlying
                 architecture of the digital computer. Evolutionary
                 operators were devised to permit the adaptive
                 complexification of gridbrains.

                 The SEEA algorithm enables the embedding of
                 evolutionary processes in a continuous multiagent
                 simulation in a non-intrusive way. Co-evolution of
                 multiple species is possible. Two bioinspired
                 extensions to the base algorithm are proposed, with the
                 goal of promoting the emergence of cooperative
                 behaviours.

                 The LabLOVE tool provides an object model where
                 simulation scenarios are defined by way of local
                 interactions. The representation of simulation object
                 features as symbols mitigates the need for pre-defined
                 agent sensory and action interfaces. This increases the
                 freedom of evolutionary processes to generate
                 diversified behaviors.

                 Experimental results are presented, where our models
                 are validated. The role of the several genetic
                 operators and evolutionary parameters is analysed and
                 discussed. Insights are gained, like the role of our
                 recombination operator in bloat control or the
                 importance of neutral search. In scenarios that require
                 cooperation, we demonstrate the emergence of
                 synchronisation behaviours that would be difficult to
                 achieve under conventional approaches. Kin selection
                 and group selection based approaches are compared. In a
                 scenario where two species are in competition, we
                 demonstrated the emergence of specialisation niches
                 without the need for geographical isolation.",
  notes =        "Thesis submitted to the University of Coimbra in
                 partial fulfilment of the requirements for the degree
                 of Doctor of Philosophy in Informatics
                 Engineering.

                 Supervisor Doctor Ernesto Jorge Fernandes Costa Full
                 Professor of the Department of Informatics Engineering
                 of the Faculty of Sciences and Technology of the
                 University of Coimbra)",
}

Genetic Programming entries for Telmo Menezes

Citations