The Compressed Differential Heuristic

Authors

  • Meir Goldenberg Ben-Gurion University
  • Nathan Sturtevant University of Denver
  • Ariel Felner Ben-Gurion University
  • Jonathan Schaeffer University of Alberta

Abstract

The differential heuristic (DH) is an effective memory-based heuristic for explicit state spaces. In this paper we aim to improve its performance and memory usage. We introduce a compression method for DHs which stores only a portion of the original uncompressed DH, while preserving enough information to enable efficient search. Compressed DHs (CDH) are flexible and can be tuned to fit any size of memory, even smaller than the size of the state space. Furthermore, CDHs can be built without the need to create and store the entire uncompressed DH. Experimental results across different domains show that, for a given amount of memory, a CDH significantly outperforms an uncompressed DH.

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Published

2011-08-04

How to Cite

Goldenberg, M., Sturtevant, N., Felner, A., & Schaeffer, J. (2011). The Compressed Differential Heuristic. Proceedings of the AAAI Conference on Artificial Intelligence, 25(1), 24-29. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/7823

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Section

Constraints, Satisfiability, and Search