Heuristic subset selection in classical planning

Lelis HS Levi; Santiago Franco; Marvin Abisrror; Mike Barley; Sandra Zilles; Robert Holte

Heuristic subset selection in classical planning

Lelis HS Levi, Santiago Franco, Marvin Abisrror, Mike Barley, Sandra Zilles, Robert Holte

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

Abstract

In this paper we present greedy methods for selecting a subset of heuristic functions for guiding A*search. Our methods are able to optimize various objective functions while selecting a subset from a pool of up to thousands of heuristics. Specifically,our methods minimize approximations of A*’s search tree size, and approximations of A*’s running time. We show empirically that our methods can outperform state-of-the-art planners for deterministic optimal planning.

Original language	English
Title of host publication	Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI-16)
Editors	Subbarao Kambhampati
Publisher	AAAI press
Pages	3185-3195
Number of pages	11
ISBN (Print)	9781577357704
Publication status	Published - 2016
Externally published	Yes
Event	25th International Joint Conference on Artificial Intelligence - New York City, United States Duration: 9 Jul 2016 → 15 Jul 2016 Conference number: 25 http://ijcai-16.org/ (Link to Conference Website)

Conference

Conference	25th International Joint Conference on Artificial Intelligence
Abbreviated title	IJCAI 2016
Country/Territory	United States
City	New York City
Period	9/07/16 → 15/07/16
Internet address	http://ijcai-16.org/ (Link to Conference Website)

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http://www.ijcai.org/Proceedings/16/Papers/451.pdfLicence: Unspecified
http://eprints.hud.ac.uk/id/eprint/32139/Licence: Unspecified

Cite this

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title = "Heuristic subset selection in classical planning",

abstract = "In this paper we present greedy methods for selecting a subset of heuristic functions for guiding A*search. Our methods are able to optimize various objective functions while selecting a subset from a pool of up to thousands of heuristics. Specifically,our methods minimize approximations of A*{\textquoteright}s search tree size, and approximations of A*{\textquoteright}s running time. We show empirically that our methods can outperform state-of-the-art planners for deterministic optimal planning.",

author = "Levi, {Lelis HS} and Santiago Franco and Marvin Abisrror and Mike Barley and Sandra Zilles and Robert Holte",

year = "2016",

language = "English",

isbn = "9781577357704",

pages = "3185--3195",

editor = "Subbarao Kambhampati",

booktitle = "Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI-16)",

publisher = "AAAI press",

address = "United States",

note = "25th International Joint Conference on Artificial Intelligence, IJCAI 2016 ; Conference date: 09-07-2016 Through 15-07-2016",

url = "http://ijcai-16.org/",

}

Levi, LHS, Franco, S, Abisrror, M, Barley, M, Zilles, S & Holte, R 2016, Heuristic subset selection in classical planning. in S Kambhampati (ed.), Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI-16). AAAI press, pp. 3185-3195, 25th International Joint Conference on Artificial Intelligence, New York City, New York, United States, 9/07/16. <http://www.ijcai.org/Proceedings/16/Papers/451.pdf>

Heuristic subset selection in classical planning. / Levi, Lelis HS; Franco, Santiago; Abisrror, Marvin et al.

Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI-16). ed. / Subbarao Kambhampati. AAAI press, 2016. p. 3185-3195.

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

TY - GEN

T1 - Heuristic subset selection in classical planning

AU - Levi, Lelis HS

AU - Franco, Santiago

AU - Abisrror, Marvin

AU - Barley, Mike

AU - Zilles, Sandra

AU - Holte, Robert

N1 - Conference code: 25

PY - 2016

Y1 - 2016

N2 - In this paper we present greedy methods for selecting a subset of heuristic functions for guiding A*search. Our methods are able to optimize various objective functions while selecting a subset from a pool of up to thousands of heuristics. Specifically,our methods minimize approximations of A*’s search tree size, and approximations of A*’s running time. We show empirically that our methods can outperform state-of-the-art planners for deterministic optimal planning.

AB - In this paper we present greedy methods for selecting a subset of heuristic functions for guiding A*search. Our methods are able to optimize various objective functions while selecting a subset from a pool of up to thousands of heuristics. Specifically,our methods minimize approximations of A*’s search tree size, and approximations of A*’s running time. We show empirically that our methods can outperform state-of-the-art planners for deterministic optimal planning.

UR - http://www.ijcai.org/Proceedings/2016

M3 - Conference contribution

SN - 9781577357704

SP - 3185

EP - 3195

BT - Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI-16)

A2 - Kambhampati, Subbarao

PB - AAAI press

T2 - 25th International Joint Conference on Artificial Intelligence

Y2 - 9 July 2016 through 15 July 2016

ER -

Heuristic subset selection in classical planning

Abstract

Conference

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