Learning to win: case-based plan selection in a real-time strategy game

  • Authors:

  • David W. Aha;Matthew Molineaux;Marc Ponsen

  • Affiliations:

  • Navy Center for Applied Research in Artificial Intelligence, Naval Research Laboratory (Code 5515), Washington, DC;AES Division, ITT Industries, Alexandria, VA;Department of Computer Science and Engineering, Lehigh University, Bethlehem, PA

  • Venue:
  • ICCBR'05 Proceedings of the 6th international conference on Case-Based Reasoning Research and Development
  • Year:
  • 2005

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Abstract

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.