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Real-time adaptive A*

Published: 08 May 2006 Publication History

Abstract

Characters in real-time computer games need to move smoothly and thus need to search in real time. In this paper, we describe a simple but powerful way of speeding up repeated A* searches with the same goal states, namely by updating the heuristics between A* searches. We then use this technique to develop a novel real-time heuristic search method, called Real-Time Adaptive A*, which is able to choose its local search spaces in a fine-grained way. It updates the values of all states in its local search spaces and can do so very quickly. Our experimental results for characters in real-time computer games that need to move to given goal coordinates in unknown terrain demonstrate that this property allows Real-Time Adaptive A* to follow trajectories of smaller cost for given time limits per search episode than a recently proposed real-time heuristic search method [5] that is more difficult to implement.

References

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V. Bulitko and G. Lee. Learning in real-time search: A unifying framework. Journal of Artificial Intelligence Research, page (in press), 2005.
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T. Ishida. Real-Time Search for Learning Autonomous Agents. Kluwer Academic Publishers, 1997.
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S. Koenig. A comparison of fast search methods for real-time situated agents. In Proceedings of the International Conference on Autonomous Agents and Multi-Agent Systems, pages 864--871, 2004.
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S. Koenig and M. Likhachev. D* Lite. In Proceedings of the National Conference on Artificial Intelligence, pages 476--483, 2002.
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S. Koenig and M. Likhachev. Adaptive A* {poster abstract}. In Proceedings of the International Conference on Autonomous Agents and Multi-Agent Systems, pages 1311--1312, 2005.
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S. Koenig, C. Tovey, and Y. Smirnov. Performance bounds for planning in unknown terrain. Artificial Intelligence, 147:253--279, 2003.
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R. Korf. Real-time heuristic search. Artificial Intelligence, 42(2--3):189--211, 1990.
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J. Pearl. Heuristics: Intelligent Search Strategies for Computer Problem Solving. Addison-Wesley, 1985.
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cover image ACM Conferences
AAMAS '06: Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
May 2006
1631 pages
ISBN:1595933034
DOI:10.1145/1160633
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 08 May 2006

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Author Tags

  1. A*
  2. D* lite
  3. action and planning in agents
  4. agent planning
  5. games
  6. heuristic search
  7. incremental search
  8. perception
  9. planning with the freespace assumption
  10. real-time decision making

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  • (2024)Double Layer A*: An Emergency Path Planning Model Based on Map Grid and Double Layer Search StructureIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.337082825:9(11509-11521)Online publication date: Sep-2024
  • (2024)A multi-algorithm pathfinding method: Exploiting performance variations for enhanced efficiencyAnnals of Mathematics and Artificial Intelligence10.1007/s10472-024-09957-3Online publication date: 14-Nov-2024
  • (2023)Evaluating Heuristic Search Algorithms in Pathfinding: A Comprehensive Study on Performance Metrics and Domain ParametersElectronic Proceedings in Theoretical Computer Science10.4204/EPTCS.391.12391(102-112)Online publication date: 30-Sep-2023
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