On the online generation of effective macro-operators

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

Abstract

Macro-operator ("macro", for short) generation is a well-known technique that is used to speed-up the planning process. Most published work on using macros in automated planning relies on an offline learning phase where training plans, that is, solutions of simple problems, are used to generate the macros. However, there might not always be a place to accommodate training. In this paper we propose OMA, an efficient method for generating useful macros without an offline learning phase, by utilising lessons learnt from existing macro learning techniques. Empirical evaluation with IPC benchmarks demonstrates performance improvement in a range of state-of-the-art planning engines, and provides insights into what macros can be generated without training.

LanguageEnglish
Title of host publicationIJCAI 2015 - Proceedings of the 24th International Joint Conference on Artificial Intelligence
PublisherInternational Joint Conferences on Artificial Intelligence
Pages1544-1550
Number of pages7
Volume2015-January
ISBN (Electronic)9781577357384
Publication statusPublished - 2015
Event24th International Joint Conference on Artificial Intelligence - Buenos Aires, Argentina
Duration: 25 Jul 201531 Jul 2015
Conference number: 24
http://www.ijcai.org/past_conferences (Link to Conference Website )

Conference

Conference24th International Joint Conference on Artificial Intelligence
Abbreviated titleIJCAI 2015
CountryArgentina
CityBuenos Aires
Period25/07/1531/07/15
Internet address

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Macros
Planning
Engines

Cite this

Chrpa, L., Vallati, M., & McCluskey, T. L. (2015). On the online generation of effective macro-operators. In IJCAI 2015 - Proceedings of the 24th International Joint Conference on Artificial Intelligence (Vol. 2015-January, pp. 1544-1550). International Joint Conferences on Artificial Intelligence.
Chrpa, Lukáš ; Vallati, Mauro ; McCluskey, Thomas Leo. / On the online generation of effective macro-operators. IJCAI 2015 - Proceedings of the 24th International Joint Conference on Artificial Intelligence. Vol. 2015-January International Joint Conferences on Artificial Intelligence, 2015. pp. 1544-1550
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Chrpa, L, Vallati, M & McCluskey, TL 2015, On the online generation of effective macro-operators. in IJCAI 2015 - Proceedings of the 24th International Joint Conference on Artificial Intelligence. vol. 2015-January, International Joint Conferences on Artificial Intelligence, pp. 1544-1550, 24th International Joint Conference on Artificial Intelligence, Buenos Aires, Argentina, 25/07/15.

On the online generation of effective macro-operators. / Chrpa, Lukáš; Vallati, Mauro; McCluskey, Thomas Leo.

IJCAI 2015 - Proceedings of the 24th International Joint Conference on Artificial Intelligence. Vol. 2015-January International Joint Conferences on Artificial Intelligence, 2015. p. 1544-1550.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AB - Macro-operator ("macro", for short) generation is a well-known technique that is used to speed-up the planning process. Most published work on using macros in automated planning relies on an offline learning phase where training plans, that is, solutions of simple problems, are used to generate the macros. However, there might not always be a place to accommodate training. In this paper we propose OMA, an efficient method for generating useful macros without an offline learning phase, by utilising lessons learnt from existing macro learning techniques. Empirical evaluation with IPC benchmarks demonstrates performance improvement in a range of state-of-the-art planning engines, and provides insights into what macros can be generated without training.

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Chrpa L, Vallati M, McCluskey TL. On the online generation of effective macro-operators. In IJCAI 2015 - Proceedings of the 24th International Joint Conference on Artificial Intelligence. Vol. 2015-January. International Joint Conferences on Artificial Intelligence. 2015. p. 1544-1550