Creating AI Characters for Fighting Games using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  author =       "Giovanna Martinez-Arellano and Richard Cant and 
                 David Woods",
  journal =      "IEEE Transactions on Computational Intelligence and AI
                 in Games",
  title =        "Creating AI Characters for Fighting Games using
                 Genetic Programming",
  abstract =     "This paper proposes a character generation approach
                 for the M.U.G.E.N. fighting game that can create
                 engaging AI characters using a computationally cheap
                 process without the intervention of the expert
                 developer. The approach uses a Genetic Programming
                 algorithm that refines randomly generated character
                 strategies into better ones using tournament selection.
                 The generated AI characters were tested by twenty-seven
                 human players and were rated according to results,
                 perceived difficulty and how engaging the game play
                 was. The main advantages of this procedure are that no
                 prior knowledge of how to code the strategies of the AI
                 character is needed and there is no need to interact
                 with the internal code of the game. In addition, the
                 procedure is capable of creating a wide diversity of
                 players with different strategic skills, which could be
                 potentially used as a starting point to a further
                 adaptive process.",
  keywords =     "genetic algorithms, genetic programming, Adaptation
                 models, Games, Learning (artificial intelligence),
                 Real-time systems, AI, character, fighting games",
  DOI =          "doi:10.1109/TCIAIG.2016.2642158",
  ISSN =         "1943-068X",
  notes =        "Also known as \cite{7792145}",

Genetic Programming entries for Giovanna Martinez-Arellano Richard Cant David Woods