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Simple optimization techniques for A*-based search
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International Conference on Autonomous Agents archive
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2 table of contents
Budapest, Hungary
SESSION: Planning/search table of contents
Pages 931-936  
Year of Publication: 2009
ISBN:978-0-9817381-7-8
Authors
Xiaoxun Sun  University of Southern California, Los Angeles, CA
William Yeoh  University of Southern California, Los Angeles, CA
Po-An Chen  University of Southern California, Los Angeles, CA
Sven Koenig  University of Southern California, Los Angeles, CA
Sponsors
: The Foundation for Intelligent Physical Agents
Microsoft Research : Microsoft Research
: Whitestein Technologies
: European Office of Aerospace Research and Development, Air Force Office of Scientific Research, United States Air Force Research Laboratory
: Drexel University
: Wiley -- Blackwell Ltd
Publisher
Bibliometrics
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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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P. Hart, N. Nilsson, and B. Raphael. A formal basis for the heuristic determination of minimum cost paths. IEEE Transactions on Systems Science and Cybernetics, 2:100--107, 1968.
 
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M. Likhachev, G. Gordon, and S. Thrun. ARA*: Anytime A* with provable bounds on sub-optimality. In Proceedings of the Neural Information Processing Systems Conference, 2003.
 
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X. Sun, M. Druzdzel, and C. Yuan. Dynamic Weighting A* search-based MAP algorithm for Bayesian networks. In Proceedings of the International Joint Conference on Artificial Intelligence, pages 2385--2390, 2007.
 
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Collaborative Colleagues:
Xiaoxun Sun: colleagues
William Yeoh: colleagues
Po-An Chen: colleagues
Sven Koenig: colleagues