Evolving board evaluation fuctions for a complex strategy game

Created by W.Langdon from gp-bibliography.bib Revision:1.4394

  title =        "Evolving board evaluation fuctions for a complex
                 strategy game",
  author =       "Lisa Patricia Anthony",
  year =         "2002",
  month =        dec # "~30",
  language =     "en_US",
  school =       "Drexel University",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://dspace.library.drexel.edu/handle/1721.1/18",
  URL =          "http://dspace.library.drexel.edu/bitstream/1860/18/1/anthony_thesis.pdf",
  size =         "73 pages",
  abstract =     "The development of board evaluation functions for
                 complex strategy games has been approached in a variety
                 of ways. The analysis of game interactions is
                 recognized as a valid analogy to common real-world
                 problems, which often present difficulty in designing
                 algorithms to solve them. Genetic programming, as a
                 branch of evolutionary computation, provides advantages
                 over traditional algorithms in solving these complex
                 real-world problems in speed, robustness and
                 flexibility. This thesis attempts to address the
                 problem of applying genetic programming techniques to
                 the evolution of a strategy for evaluating potential
                 moves in a one-step lookahead intelligent agent
                 heuristic for a complex strategybased game. This is
                 meant to continue the work in artificial intelligence
                 which seeks to provide computer systems with the tools
                 they need to learn how to operate within a domain,
                 given only the basic building blocks.

                 The issues surrounding this problem are formulated and
                 techniques are presented within the realm of genetic
                 programming which aim to contribute to the solution of
                 this problem. The domain chosen is the strategy game
                 known as Acquire, whose object is to amass wealth while
                 investing stock in hotel chains and effecting mergers
                 of these chains as they grow. The evolution of the
                 board evaluation functions to be used by agent players
                 of the game is accomplished via genetic programming.
                 Implementation details are discussed, empirical results
                 are presented, and the strategies of some of the best
                 players are analyzed. Future improvements on these
                 techniques within this domain are outlined, as well as
                 implications for artificial intelligence and genetic
  notes =        "format = 318461",

Genetic Programming entries for Lisa Patricia Anthony