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.
| Original language | English |
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| Title of host publication | Proceedings of the 25th AAAI Conference on Artificial Intelligence, AAAI 2011 |
| Publisher | AAAI Press |
| Pages | 24-29 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781577355083 |
| State | Published - 11 Aug 2011 |
| Externally published | Yes |
| Event | 25th AAAI Conference on Artificial Intelligence, AAAI 2011 - San Francisco, United States Duration: 7 Aug 2011 → 11 Aug 2011 |
Publication series
| Name | Proceedings of the 25th AAAI Conference on Artificial Intelligence, AAAI 2011 |
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Conference
| Conference | 25th AAAI Conference on Artificial Intelligence, AAAI 2011 |
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| Country/Territory | United States |
| City | San Francisco |
| Period | 7/08/11 → 11/08/11 |
Bibliographical note
Publisher Copyright:Copyright © 2011, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
ASJC Scopus subject areas
- Artificial Intelligence