Memory-based heuristics for explicit state spaces

  • Authors:

  • Nathan R. Sturtevant;Ariel Felner;Max Barrer;Jonathan Schaeffer;Neil Burch

  • Affiliations:

  • Computing Science, University of Alberta, Edmonton, AB, Canada;Information Systems Engineering, Deutsche Telekom Labs, Ben-Gurion University, Be'er-Sheva, Israel;Information Systems Engineering, Deutsche Telekom Labs, Ben-Gurion University, Be'er-Sheva, Israel;Computing Science, University of Alberta, Edmonton, AB, Canada;Computing Science, University of Alberta, Edmonton, AB, Canada

  • Venue:
  • IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
  • Year:
  • 2009

Quantified Score

Hi-index 0.00

Visualization

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.