Behavior control of multiple agents by Cartesian Genetic Programming equipped with sharing sub-programs among agents

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

  author =       "Akira Hara and Jun-ichi Kushida and Tomoya Okita and 
                 Tetsuyuki Takahama",
  booktitle =    "8th IEEE International Workshop on Computational
                 Intelligence and Applications (IWCIA)",
  title =        "Behavior control of multiple agents by Cartesian
                 Genetic Programming equipped with sharing sub-programs
                 among agents",
  year =         "2015",
  pages =        "71--76",
  address =      "Hiroshima, Japan",
  keywords =     "genetic algorithms, genetic programming, Artificial
                 neural networks, Cloning, Mathematical model,
                 Multi-agent systems, Optimisation, Cartesian Genetic
                 Programming, Evolutionary Computation, Multi-Agent
  URL =          "",
  DOI =          "doi:10.1109/IWCIA.2015.7449465",
  ISSN =         "1883-3977",
  month =        "6-7 " # nov,
  abstract =     "In this paper, we focus on evolutionary optimisation
                 of multi-agent behaviour. There are two representative
                 models for multi-agent control, homogeneous and
                 heterogeneous models. In the homogeneous model, all
                 agents are controlled by the same controller.
                 Therefore, it is difficult to realize complex
                 cooperative behaviour such as division of labours. In
                 contrast, in the heterogeneous model, respective agents
                 can play different roles for cooperative tasks.
                 However, the search space becomes too large to optimise
                 respective controllers. To solve the problems, we
                 propose a new multi-agent control model based on
                 Cartesian Genetic Programming (CGP). In CGP, each
                 individual represents a graph-structural program and it
                 can have multiple outputs. The feature is used for
                 controlling multiple agents in our model. In addition,
                 we propose a new genetic operator dedicated to
                 multi-agent control. Our method enables multiple agents
                 to not only take different actions according to their
                 own roles but also share sub-programs if the same
                 behaviour is needed for solving problems. We applied
                 our method to a food foraging problem. The experimental
                 results showed that the performance of our method is
                 superior to those of the conventional models.",
  notes =        "Also known as \cite{7449465}",

Genetic Programming entries for Akira Hara Jun-ichi Kushida Tomoya Okita Tetsuyuki Takahama