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Session Name Rolling the Dice: Leveraging Monte-Carlo Tree Search in Game AI
Speaker(s) Nathan Sturtevant, Jeff Rollason, Peter Cowling, David Churchill
Company Name(s) University of Denver, AI Factory, Ltd., University of York, UK, University of Alberta, Canada
Track / Format AI Summit
Overview As the complexity of potential state spaces that AI agents have to explore moves beyond more traditional games like chess, search-based approaches like minimax are no longer feasible to employ. Worse, when imperfect information is involved, the problem becomes largely intractable. What is needed is a way of exploring that multi-layered possibility space and coming up with a "good enough" answer, even if it isn't the "mathematically perfect" one, and still do it in a reasonable amount of time. Using examples from a suite of successful commercial mobile games, as well as the winner of the annual StarCraft AI Competition, this session explains how Monte-Carlo Tree Search (MCTS) works and how it has become a viable tool for AI agents in a wide variety of games.

GDC 2014

Nathan Sturtevant

University of Denver

Jeff Rollason

AI Factory, Ltd.

Peter Cowling

University of York, UK

David Churchill

University of Alberta, Canada

free content

AI Summit

AI


UBM Tech