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 first application of

TIELT

. We describe this application, our lessons learned, and our motivations for future work.