The Compressed Differential Heuristic

Meir Goldenberg; Nathan Sturtevant; Ariel Felner; Jonathan Schaeffer

The Compressed Differential Heuristic

Meir Goldenberg, Nathan Sturtevant, Ariel Felner, Jonathan Schaeffer

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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
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

Conference

Conference	25th AAAI Conference on Artificial Intelligence, AAAI 2011
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

Cite this

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title = "The Compressed Differential Heuristic",

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.",

author = "Meir Goldenberg and Nathan Sturtevant and Ariel Felner and Jonathan Schaeffer",

note = "Publisher Copyright: Copyright {\textcopyright} 2011, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.; 25th AAAI Conference on Artificial Intelligence, AAAI 2011 ; Conference date: 07-08-2011 Through 11-08-2011",

year = "2011",

month = aug,

day = "11",

language = "English",

series = "Proceedings of the 25th AAAI Conference on Artificial Intelligence, AAAI 2011",

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pages = "24--29",

booktitle = "Proceedings of the 25th AAAI Conference on Artificial Intelligence, AAAI 2011",

}

Goldenberg, M, Sturtevant, N, Felner, A & Schaeffer, J 2011, The Compressed Differential Heuristic. in Proceedings of the 25th AAAI Conference on Artificial Intelligence, AAAI 2011. Proceedings of the 25th AAAI Conference on Artificial Intelligence, AAAI 2011, AAAI Press, pp. 24-29, 25th AAAI Conference on Artificial Intelligence, AAAI 2011, San Francisco, United States, 7/08/11.

The Compressed Differential Heuristic. / Goldenberg, Meir; Sturtevant, Nathan; Felner, Ariel et al.
Proceedings of the 25th AAAI Conference on Artificial Intelligence, AAAI 2011. AAAI Press, 2011. p. 24-29 (Proceedings of the 25th AAAI Conference on Artificial Intelligence, AAAI 2011).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

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AU - Schaeffer, Jonathan

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N2 - 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.

AB - 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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ER -

The Compressed Differential Heuristic

Abstract

Publication series

Conference

Bibliographical note

ASJC Scopus subject areas

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Cite this