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Thesis
Evolving board evaluation fuctions for a complex strategy game
Master of Science (M.S.), Drexel University
Sep 2002
DOI:
https://doi.org/10.17918/etd-18
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 strategy based 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.
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Details
- Title
- Evolving board evaluation fuctions for a complex strategy game
- Creators
- Lisa Patricia Anthony - DU
- Contributors
- William Clement Regli (Advisor) - Drexel University (1970-)
- Awarding Institution
- Drexel University
- Degree Awarded
- Master of Science (M.S.)
- Publisher
- Drexel University; Philadelphia, Pennsylvania
- Resource Type
- Thesis
- Language
- English
- Academic Unit
- College of Arts and Sciences; Drexel University; Mathematics
- Other Identifier
- 18; 991014632208204721