| Simple optimization techniques for A*-based search |
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International Conference on Autonomous Agents
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Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
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Budapest, Hungary
SESSION: Planning/search
table of contents
Pages 931-936
Year of Publication: 2009
ISBN:978-0-9817381-7-8
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Authors
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Xiaoxun Sun
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University of Southern California, Los Angeles, CA
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William Yeoh
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University of Southern California, Los Angeles, CA
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Po-An Chen
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University of Southern California, Los Angeles, CA
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Sven Koenig
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University of Southern California, Los Angeles, CA
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Downloads (6 Weeks): 14, Downloads (12 Months): 26, Citation Count: 0
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ABSTRACT
In this paper, we present two simple optimizations that can reduce the number of priority queue operations for A* and its extensions. Basically, when the optimized search algorithms expand a state, they check whether they will expand a successor of the state next. If so, they do not first insert it into the priority queue and then immediately remove it again. These changes might appear to be trivial but are well suited for Generalized Adaptive A*, an extension of A*. Our experimental results indeed show that they speed up Generalized Adaptive A* by up to 30 percent if its priority queue is implemented as a binary heap.
REFERENCES
Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.
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