Evolving Bot's AI in Unreal

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

  author =       "Antonio Mora and Juan Julian Merelo and 
                 Ramon Montoya and Pablo Garcia and Pedro Castillo and 
                 Juan Luis Jimenez and Anna Esparcia and Ana Martinez",
  title =        "Evolving Bot's AI in Unreal",
  booktitle =    "EvoGAMES",
  year =         "2010",
  editor =       "Cecilia {Di Chio} and Stefano Cagnoni and 
                 Carlos Cotta and Marc Ebner and Aniko Ekart and 
                 Anna I. Esparcia-Alcazar and Chi-Keong Goh and 
                 Juan J. Merelo and Ferrante Neri and Mike Preuss and 
                 Julian Togelius and Georgios N. Yannakakis",
  volume =       "6024",
  series =       "LNCS",
  pages =        "171--180",
  address =      "Istanbul",
  month =        "7-9 " # apr,
  organisation = "EvoStar",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-642-12238-5",
  DOI =          "doi:10.1007/978-3-642-12239-2_18",
  abstract =     "This paper describes the design, implementation and
                 results of an evolutionary bot inside the PC game
                 Unreal, that is, an autonomous enemy which tries to
                 beat the human player and/or some other bots. The
                 default artificial intelligence (AI) of this bot has
                 been improved using two different evolutionary methods:
                 genetic algorithms (GAs) and genetic programming (GP).
                 The first one has been applied for tuning the
                 parameters of the hard-coded values inside the bot AI
                 code. The second method has been used to change the
                 default set of rules (or states) that defines its
                 behaviour. Both techniques yield very good results,
                 evolving bots which are capable to beat the default
                 ones. The best results are yielded for the GA approach,
                 since it just does a refinement following the default
                 behaviour rules, while the GP method has to redefine
                 the whole set of rules, so it is harder to get good
  notes =        "EvoGAMES'2010 held in conjunction with EuroGP'2010
                 EvoCOP2010 EvoBIO2010",

Genetic Programming entries for Antonio M Mora Garcia Juan Julian Merelo Ramon Montoya Pablo Garcia-Sanchez Pedro A Castillo Valdivieso Juan Luis Jimenez Anna Esparcia-Alcazar Anais Martinez