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ICCBR
2005
Springer

Learning to Win: Case-Based Plan Selection in a Real-Time Strategy Game

9 years 11 months ago
Learning to Win: Case-Based Plan Selection in a Real-Time Strategy Game
While several researchers have applied case-based reasoning techniques to games, only Ponsen and Spronck (2004) have addressed the challenging problem of learning to win real-time games. Focusing on WARGUS, they report good results for a genetic algorithm that searches in plan space, and for a weighting algorithm (dynamic scripting) that biases subplan retrieval. However, both approaches assume a static opponent, and were not designed to transfer their learned knowledge to opponents with substantially different strategies. We introduce a plan retrieval algorithm that, by using three key sources of domain knowledge, removes the assumption of a static opponent. Our experiments show that its implementation in the Case-based Tactician (CAT) significantly outperforms the best among a set of genetically evolved plans when tested against random WARGUS opponents. CAT communicates with WARGUS through TIELT, a testbed for integrating and evaluating decision systems with simulators. This is the f...
David W. Aha, Matthew Molineaux, Marc J. V. Ponsen
Added 27 Jun 2010
Updated 27 Jun 2010
Type Conference
Year 2005
Where ICCBR
Authors David W. Aha, Matthew Molineaux, Marc J. V. Ponsen
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