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Combining AI Methods for Learning Bots in a Real Time Strategy Game

Baumgarten, Robin; Colton, Simon and Morris, Mark. 2009. Combining AI Methods for Learning Bots in a Real Time Strategy Game. International Journal of Computer Games Technology, 2009(129075), pp. 1-10. ISSN 1687-7047 [Article]

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Abstract or Description

We describe an approach for simulating human game-play in strategy games using a variety of AI techniques, including simulated annealing, decision tree learning, and case-based reasoning. We have implemented an AI-bot that uses these techniques to form a novel approach for planning fleet movements and attacks in DEFCON, a nuclear war simulation strategy game released in 2006 by Introversion Software Ltd. The AI-bot retrieves plans from a case-base of recorded games, then uses these to generate a new plan using a method based on decision tree learning. In addition, we have implemented more sophisticated control over low-level actions that enable the AI-bot to synchronize bombing runs, and used a simulated annealing approach for assigning bombing targets to planes and opponent cities to missiles. We describe how our AI-bot operates, and the experimentation we have performed in order to determine an optimal configuration for it. With this configuration, our AI-bot beats Introversion's finite state machine automated player in 76.7% of 150 matches played. We briefly introduce the notion of ability versus enjoyability and discuss initial results of a survey we conducted with human players.

Item Type:

Article

Identification Number (DOI):

https://doi.org/10.1155/2009/129075

Departments, Centres and Research Units:

Computing
Research Office > REF2014

Dates:

DateEvent
2009Published

Item ID:

9179

Date Deposited:

18 Oct 2013 14:05

Last Modified:

09 Jul 2018 22:12

Peer Reviewed:

Yes, this version has been peer-reviewed.

URI:

http://research.gold.ac.uk/id/eprint/9179

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