Learning Environment Models in Car Racing Using Stateful Genetic Programming

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

@InProceedings{Agapitos:2011:CIG,
  author =       "Alexandros Agapitos and Michael O'Neill and 
                 Anthony Brabazon and Theodoros Theodoridis",
  title =        "Learning Environment Models in Car Racing Using
                 Stateful Genetic Programming",
  booktitle =    "Proceedings of the 2011 IEEE Conference on
                 Computational Intelligence and Games",
  year =         "2011",
  address =      "Seoul, South Korea",
  pages =        "219--226",
  month =        "31 " # aug # " - 3 " # sep,
  publisher =    "IEEE",
  keywords =     "genetic algorithms, genetic programming, Reinforcement
                 Learning, Multiobjective Evolution, AI in Computer
                 Games, Car Racing, AI game agent, computational
                 intelligence, diverse opponent generation, game play
                 learning, nonplayer character, computer games,
                 evolutionary computation, learning (artificial
                 intelligence), multi-agent systems, 2D data structures,
                 artificial agents, car racing games, learning
                 environment models, model building behaviour, modular
                 programs, non player characters, cognition, computer
                 games, data structures, learning (artificial
                 intelligence), multi-agent systems",
  isbn13 =       "978-1-4577-0010-1",
  URL =          "http://cilab.sejong.ac.kr/cig2011/proceedings/CIG2011/papers/paper54.pdf",
  DOI =          "doi:10.1109/CIG.2011.6032010",
  size =         "8 pages",
  abstract =     "For computational intelligence to be useful in
                 creating game agent AI we need to focus on methods that
                 allow the creation and maintenance of models for the
                 environment, which the artificial agents inhabit.
                 Maintaining a model allows an agent to plan its actions
                 more effectively by combining immediate sensory
                 information along with a memories that have been
                 acquired while operating in that environment. To this
                 end, we propose a way to build environment models for
                 non-player characters in car racing games using
                 stateful Genetic Programming. A method is presented,
                 where general-purpose 2-dimensional data-structures are
                 used to build a model of the racing track. Results
                 demonstrate that model-building behaviour can be
                 cooperatively coevolved with car-controlling behaviour
                 in modular programs that make use of these models in
                 order to navigate successfully around a racing track.",
  notes =        "Indexed memory.

                 Also known as \cite{6032010}",
}

Genetic Programming entries for Alexandros Agapitos Michael O'Neill Anthony Brabazon Theodoros Theodoridis

Citations