Designing competitive bots for a real time strategy game using genetic programming

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

  author =       "Antonio Fernandez-Ares and Pablo Garcia-Sanchez and 
                 Antonio Miguel Mora and Pedro A. Castillo and 
                 Juan Julian Merelo Guervos",
  title =        "Designing competitive bots for a real time strategy
                 game using genetic programming",
  booktitle =    "Proceedings 1st Congreso de la Sociedad Espanola para
                 las Ciencias del Videojuego, CoSECivi 2014",
  year =         "2014",
  editor =       "David Camacho and Marco Antonio Gomez-Martin and 
                 Pedro Antonio Gonzalez-Calero",
  series =       "CEUR Workshop Proceedings",
  volume =       "1196",
  pages =        "159--172",
  address =      "Barcelona, Spain",
  month =        jun # " 24",
  publisher =    "",
  keywords =     "genetic algorithms, genetic programming",
  bibsource =    "dblp computer science bibliography,",
  URL =          "",
  URL =          "",
  size =         "14 pages",
  abstract =     "The design of the Artificial Intelligence (AI) engine
                 for an autonomous agent (bot) in a game is always a
                 difficult task mainly done by an expert human player,
                 who has to transform his/her knowledge into a
                 behavioural engine. This paper presents an approach for
                 conducting this task by means of Genetic Programming
                 (GP) application. This algorithm is applied to design
                 decision trees to be used as bot's AI in 1 vs 1 battles
                 inside the RTS game Planet Wars. Using this method it
                 is possible to create rule-based systems defining
                 decisions and actions, in an automatic way, completely
                 different from a human designer doing them from
                 scratch. These rules will be optimised along the
                 algorithm run, considering the bots' performance during
                 evaluation matches. As GP can generate and evolve
                 behavioural rules not taken into account by an expert,
                 the obtained bots could perform better than
                 human-defined ones. Due to the difficulties when
                 applying Computational Intelligence techniques in the
                 videogames scope, such as noise factor in the
                 evaluation functions, three different fitness
                 approaches have been implemented and tested in this
                 work. Two of them try to minimise this factor by
                 considering additional dynamic information about the
                 evaluation matches, rather than just the final result
                 (the winner), as the other function does. In order to
                 prove them, the best obtained agents have been compared
                 with a previous bot, created by an expert player (from
                 scratch) and then optimised by means of Genetic
                 Algorithms. The experiments show that the three used
                 fitness functions generate bots that outperform the
                 optimised human-defined one, being the area-based
                 fitness function the one that produces better

Genetic Programming entries for Antonio Fernandez-Ares Pablo Garcia-Sanchez Antonio M Mora Garcia Pedro A Castillo Valdivieso Juan Julian Merelo