Proceedings

Memory-Based Heuristics for Explicit State Spaces

Download BibTeX Cite

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

Nathan R. Sturtevant, Ariel Felner, Max Barer, Jonathan Schaeffer, and Neil Burch. 2009. Memory-Based Heuristics for Explicit State Spaces. In Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence, IJCAI-09. IJCAI, 609–613. https://ijcai.org/proceedings/2009/107
Welcome Future Conferences AI Journal Division IJCAI Proceedings About IJCAI IJCAI Awards