Memory-Based Heuristics for Explicit State Spaces
Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence. Pages 609-613.
Abstract
In many scenarios, quickly solving a relatively small search problem with an arbitrary start and arbitrary goal state is important (e.g., GPS navigation). In order to speed this process, we introduce a new class of memory-based heuristics, called true distance heuristics, that store true distances between some pairs of states in the original state space can be used for a heuristic between any pair of states. We provide a number of techniques for using and improving true distance heuristics such that most of the benefits of the all-pairs shortest-path computation can be gained with less than 1% of the memory. Experimental results on a number of domains show a 6-14 fold improvement in search speed compared to traditional heuristics.Nathan R. Sturtevant, Ariel Felner, Max Barrer, Jonathan Schaeffer, Neil Burch
@inproceedings{ijcai2009p107,
title = {Memory-Based Heuristics for Explicit State Spaces},
author = {Sturtevant, Nathan R. and Felner, Ariel and Barer, Max and Schaeffer, Jonathan and Burch, Neil},
booktitle = {Proceedings of the Twenty-First International Joint Conference on
Artificial Intelligence, {IJCAI-09}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Hiroaki Kitano},
pages = {609--613},
year = {2009},
url = {https://ijcai.org/proceedings/2009/107},
}
TY - CPAPER TI - Memory-Based Heuristics for Explicit State Spaces AU - Sturtevant, Nathan R. AU - Felner, Ariel AU - Barer, Max AU - Schaeffer, Jonathan AU - Burch, Neil T2 - Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence, IJCAI-09 PB - International Joint Conferences on Artificial Intelligence PY - 2009 SP - 609 EP - 613 UR - https://ijcai.org/proceedings/2009/107 ER -Copied
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