Real-Time Heuristic Search with a Priority Queue
Proceedings of the Twentieth International Joint Conference on Artificial Intelligence. Pages 2372-2376.
Abstract
Learning real-time search, which interleaves planning and acting, allows agents to learn from multiple trials and respond quickly. Such algorithms require no prior knowledge of the environment and can be deployed without pre-processing. We introduce Prioritized-LRTA* (P-LRTA*), a learning real-time search algorithm based on Prioritized Sweeping. P-LRTA* focuses learning on important areas of the search space, where the importance of a state is determined by the magnitude of the updates made to neighboring states. Empirical tests on path-planning in commercial game maps show a substantial learning speed-up over state-of-the-art real-time search algorithms.
@inproceedings{ijcai2007p382,
title = {Real-Time Heuristic Search with a Priority Queue},
author = {Rayner, D. Chris and Davison, Katherine and Bulitko, Vadim and Anderson, Kenneth and Lu, Jieshan},
booktitle = {Proceedings of the Twentieth International Joint Conference on
Artificial Intelligence, {IJCAI-07}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Ramon López de Mántaras},
pages = {2372--2376},
year = {2007},
url = {https://ijcai.org/proceedings/2007/382},
}
TY - CPAPER TI - Real-Time Heuristic Search with a Priority Queue AU - Rayner, D. Chris AU - Davison, Katherine AU - Bulitko, Vadim AU - Anderson, Kenneth AU - Lu, Jieshan T2 - Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, IJCAI-07 PB - International Joint Conferences on Artificial Intelligence PY - 2007 SP - 2372 EP - 2376 UR - https://ijcai.org/proceedings/2007/382 ER -Copied
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