Applying adversarial planning techniques to go
Theoretical Computer Science
Learning procedural knowledge through observation
Proceedings of the 1st international conference on Knowledge capture
Heuristic search applied to abstract combat games
AI'05 Proceedings of the 18th Canadian Society conference on Advances in Artificial Intelligence
Game-Tree search with adaptation in stochastic imperfect-information games
CG'04 Proceedings of the 4th international conference on Computers and Games
Adversarial planning for large multi-agent simulations
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
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We introduce an adversarial planning algorithm based on game tree search, which is applicable in large-scale multiplayer domains. In order to tackle the scalability issues of game tree search, the algorithm utilizes procedural knowledge capturing how individual players tend to achieve their goals in the domain; the information is used to limit the search only to the part of the game tree that is consistent with pursuing players' goals. We impose no specific requirements on the format of the procedural knowledge; any programming language or agent specification paradigm can be employed. We evaluate the algorithm both theoretically and empirically, confirming that the proposed approach can lead to a substantial search reduction with only a minor negative impact on the quality of produced solutions.