Playing to learn: case-injected genetic algorithms for learning to play computer games

We use case-injected genetic algorithms (CIGARs) to learn to competently play computer strategy games. CIGARs periodically inject individuals that were successful in past games into the population of the GA working on the current game, biasing search toward known successful strategies. Computer strategy games are fundamentally resource allocation games characterized by complex long-term dynamics and by imperfect knowledge of the game state. CIGAR plays by extracting and solving the game's underlying resource allocation problems. We show how case injection can be used to learn to play better from a human's or system's game-playing experience and our approach to acquiring experience from human players showcases an elegant solution to the knowledge acquisition bottleneck in this domain. Results show that with an appropriate representation, case injection effectively biases the GA toward producing plans that contain important strategic elements from previously successful strategies.