Combining AI Methods for Learning Bots in a Real‐Time Strategy Game
Robin Baumgarten, Simon Colton, Mark Morris · International Journal of Computer Games Technology · 2008
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.