Conference proceeding
Adversarial Hierarchical-Task Network Planning for Complex Real-Time Games
PROCEEDINGS OF THE TWENTY-FOURTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI), pp.1652-1658
01 Jan 2015
Abstract
Real-time strategy (RTS) games are hard from an AI point of view because they have enormous state spaces, combinatorial branching factors, allow simultaneous and durative actions, and players have very little time to choose actions. For these reasons, standard game tree search methods such as alphabeta search or Monte Carlo Tree Search (MCTS) are not sufficient by themselves to handle these games. This paper presents an alternative approach called Adversarial Hierarchical Task Network (AHTN) planning that combines ideas from game tree search with HTN planning. We present the basic algorithm, relate it to existing adversarial hierarchical planning methods, and present new extensions for simultaneous and durative actions to handle RTS games. We also present empirical results for the mu RTS game, comparing it to other state of the art search algorithms for RTS games.
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Details
- Title
- Adversarial Hierarchical-Task Network Planning for Complex Real-Time Games
- Creators
- Santiago Ontanon - Drexel Univ, Philadelphia, PA 19104 USAMichael Buro - Univ Alberta, Edmonton, AB, Canada
- Contributors
- Q Yang (Editor)M Wooldridge (Editor)
- Publication Details
- PROCEEDINGS OF THE TWENTY-FOURTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI), pp.1652-1658
- Conference
- TWENTY-FOURTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI), 24th
- Publisher
- Ijcai-Int Joint Conf Artif Intell
- Number of pages
- 7
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Computer Science (Computing)
- Identifiers
- 991019170501404721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Interdisciplinary Applications