Hybrid Behaviour Orchestration in a Multilayered Cognitive Architecture Using an Evolutionary Approach

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Oscar Javier {Romero Lopez} and 
                 Angelica {de Antonio}",
  title =        "Hybrid Behaviour Orchestration in a Multilayered
                 Cognitive Architecture Using an Evolutionary Approach",
  booktitle =    "2008 IEEE World Congress on Computational
  year =         "2008",
  editor =       "Jun Wang",
  pages =        "174--180",
  address =      "Hong Kong",
  month =        "1-6 " # jun,
  organization = "IEEE Computational Intelligence Society",
  publisher =    "IEEE Press",
  isbn13 =       "978-1-4244-1823-7",
  file =         "EC0065.pdf",
  DOI =          "doi:10.1109/CEC.2008.4630795",
  abstract =     "Managing and arbitrating behaviours, processes and
                 components in multilayered cognitive architectures when
                 a huge amount of environmental variables are changing
                 continuously with increasing complexity, ensue in a
                 very comprehensive task. The presented framework
                 proposes an hybrid cognitive architecture that relies
                 on subsumption theory and includes some important
                 extensions. These extensions can be condensed in
                 inclusion of learning capabilities through bioinspired
                 reinforcement machine learning systems, an evolutionary
                 mechanism based on gene expression programming to
                 self-configure the behaviour arbitration between
                 layers, a co-evolutionary mechanism to evolve behaviour
                 repertories in a parallel fashion and finally, an
                 aggregation mechanism to combine the learning
                 algorithms outputs to improve the learning quality and
                 increase the robustness and fault tolerance ability of
                 the cognitive agent. The proposed architecture was
                 proved in an animat environment using a multi-agent
                 platform where several learning capabilities and
                 emergent properties for selfconfiguring internal
                 agent's architecture arise.",
  keywords =     "genetic algorithms, genetic programming, gene
                 expression programming",
  notes =        "WCCI 2008 - A joint meeting of the IEEE, the INNS, the
                 EPS and the IET.",

Genetic Programming entries for Oscar Javier Romero Lopez Angelica de Antonio Jimenez