Analysing the influence of the fitness function on genetically programmed bots for a real-time strategy game

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

@Article{FernandezAres:2017:EC,
  author =       "A. Fernandez-Ares and A. M. Mora and 
                 P. Garcia-Sanchez and P. A. Castillo and J. J. Merelo",
  title =        "Analysing the influence of the fitness function on
                 genetically programmed bots for a real-time strategy
                 game",
  journal =      "Entertainment Computing",
  volume =       "18",
  pages =        "15--29",
  year =         "2017",
  ISSN =         "1875-9521",
  DOI =          "doi:10.1016/j.entcom.2016.08.001",
  URL =          "http://www.sciencedirect.com/science/article/pii/S1875952116300222",
  abstract =     "Finding the global best strategy for an autonomous
                 agent (bot) in a RTS game is a hard problem, mainly
                 because the techniques applied to do this must deal
                 with uncertainty and real-time planning in order to
                 control the game agents. This work describes an
                 approach applying a Genetic Programming (GP) algorithm
                 to create the behavioural engine of bots able to play a
                 simple RTS. Normally it is impossible to know in
                 advance what kind of strategies will be the best in the
                 most general case of this problem. So GP, which
                 searches the general decision tree space, has been
                 introduced and used successfully. However, it is not
                 straightforward what fitness function would be the most
                 convenient to guide the evolutionary process in order
                 to reach the best solutions and also being less
                 sensitive to the uncertainty present in the context of
                 games. Thus, in this paper three different evaluation
                 functions have been proposed, and a detailed analysis
                 of their performance has been conducted. The paper also
                 analyses several aspects of the obtained bots, in
                 addition to their final performance on battles, such as
                 the evolution of the decision trees (behavioural
                 models) themselves, or the influence on the results of
                 noise or uncertainty. The results show that a
                 victory-based fitness, which prioritises the number of
                 victories, contributes to generate better bots, on
                 average, than other functions based on other numerical
                 aspects of the battles, such as the number of resources
                 gathered, or the number of units generated.",
  keywords =     "genetic algorithms, genetic programming, Real-time
                 strategy game, Autonomous agent, Bot, Fitness function,
                 Uncertainty",
}

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

Citations