- 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 programming.",
- notes = "format = 318461",
