Ascol

A tool for improving automatic planning domain model acquisition

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

8 Citations (Scopus)

Abstract

Intelligent agents solving problems in the real world require domain models containing widespread knowledge of the world. AI Planning requires domain models. Synthesising operator descriptions and domain specific constraints by hand for AI planning domain models is time intense, error-prone and challenging. To alleviate this, automatic domain model acquisition techniques have been introduced. Amongst others, the LOCM and LOCM2 systems require as input some plan traces only, and are effectively able to automatically encode a large part of the domain knowledge. In particular, LOCM effectively determines the dynamic part of the domain model. On the other hand, the static part of the domain – i.e., the underlying structure of the domain that can not be dynamically changed, but that affects the way in which actions can be performed – is usually missed, since it can hardly be derived by observing transitions only. In this paper we introduce ASCoL, a tool that exploits graph analysis for automatically identifying static relations, in order to enhance planning domain models. ASCoL has been evaluated on domain models generated by LOCM for international planning competition domains, and has been shown to be effective.

Original languageEnglish
Title of host publicationAIIA 2015 - Advances in Artificial Intelligence - XIVth International Conference of the Italian Association for Artificial Intelligence, Proceedings
PublisherSpringer Verlag
Pages438-451
Number of pages14
Volume9336
ISBN (Print)9783319243085
DOIs
Publication statusPublished - 2015
Event14th International Conference of the Italian Association for Artificial Intelligence - Ferrara, Italy
Duration: 23 Sep 201525 Sep 2015
Conference number: 14

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9336
ISSN (Print)03029743
ISSN (Electronic)16113349

Conference

Conference14th International Conference of the Italian Association for Artificial Intelligence
CountryItaly
CityFerrara
Period23/09/1525/09/15

Fingerprint

Domain Model
Planning
Intelligent agents
Intelligent Agents
Domain Knowledge
Acquisition
Trace
Graph in graph theory
Operator

Cite this

Jilani, R., Crampton, A., Kitchin, D., & Vallati, M. (2015). Ascol: A tool for improving automatic planning domain model acquisition. In AIIA 2015 - Advances in Artificial Intelligence - XIVth International Conference of the Italian Association for Artificial Intelligence, Proceedings (Vol. 9336, pp. 438-451). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9336). Springer Verlag. https://doi.org/10.1007/978-3-319-24309-2_33
Jilani, Rabia ; Crampton, Andrew ; Kitchin, Diane ; Vallati, Mauro. / Ascol : A tool for improving automatic planning domain model acquisition. AIIA 2015 - Advances in Artificial Intelligence - XIVth International Conference of the Italian Association for Artificial Intelligence, Proceedings. Vol. 9336 Springer Verlag, 2015. pp. 438-451 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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abstract = "Intelligent agents solving problems in the real world require domain models containing widespread knowledge of the world. AI Planning requires domain models. Synthesising operator descriptions and domain specific constraints by hand for AI planning domain models is time intense, error-prone and challenging. To alleviate this, automatic domain model acquisition techniques have been introduced. Amongst others, the LOCM and LOCM2 systems require as input some plan traces only, and are effectively able to automatically encode a large part of the domain knowledge. In particular, LOCM effectively determines the dynamic part of the domain model. On the other hand, the static part of the domain – i.e., the underlying structure of the domain that can not be dynamically changed, but that affects the way in which actions can be performed – is usually missed, since it can hardly be derived by observing transitions only. In this paper we introduce ASCoL, a tool that exploits graph analysis for automatically identifying static relations, in order to enhance planning domain models. ASCoL has been evaluated on domain models generated by LOCM for international planning competition domains, and has been shown to be effective.",
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Jilani, R, Crampton, A, Kitchin, D & Vallati, M 2015, Ascol: A tool for improving automatic planning domain model acquisition. in AIIA 2015 - Advances in Artificial Intelligence - XIVth International Conference of the Italian Association for Artificial Intelligence, Proceedings. vol. 9336, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9336, Springer Verlag, pp. 438-451, 14th International Conference of the Italian Association for Artificial Intelligence, Ferrara, Italy, 23/09/15. https://doi.org/10.1007/978-3-319-24309-2_33

Ascol : A tool for improving automatic planning domain model acquisition. / Jilani, Rabia; Crampton, Andrew; Kitchin, Diane; Vallati, Mauro.

AIIA 2015 - Advances in Artificial Intelligence - XIVth International Conference of the Italian Association for Artificial Intelligence, Proceedings. Vol. 9336 Springer Verlag, 2015. p. 438-451 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9336).

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

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AU - Jilani, Rabia

AU - Crampton, Andrew

AU - Kitchin, Diane

AU - Vallati, Mauro

PY - 2015

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N2 - Intelligent agents solving problems in the real world require domain models containing widespread knowledge of the world. AI Planning requires domain models. Synthesising operator descriptions and domain specific constraints by hand for AI planning domain models is time intense, error-prone and challenging. To alleviate this, automatic domain model acquisition techniques have been introduced. Amongst others, the LOCM and LOCM2 systems require as input some plan traces only, and are effectively able to automatically encode a large part of the domain knowledge. In particular, LOCM effectively determines the dynamic part of the domain model. On the other hand, the static part of the domain – i.e., the underlying structure of the domain that can not be dynamically changed, but that affects the way in which actions can be performed – is usually missed, since it can hardly be derived by observing transitions only. In this paper we introduce ASCoL, a tool that exploits graph analysis for automatically identifying static relations, in order to enhance planning domain models. ASCoL has been evaluated on domain models generated by LOCM for international planning competition domains, and has been shown to be effective.

AB - Intelligent agents solving problems in the real world require domain models containing widespread knowledge of the world. AI Planning requires domain models. Synthesising operator descriptions and domain specific constraints by hand for AI planning domain models is time intense, error-prone and challenging. To alleviate this, automatic domain model acquisition techniques have been introduced. Amongst others, the LOCM and LOCM2 systems require as input some plan traces only, and are effectively able to automatically encode a large part of the domain knowledge. In particular, LOCM effectively determines the dynamic part of the domain model. On the other hand, the static part of the domain – i.e., the underlying structure of the domain that can not be dynamically changed, but that affects the way in which actions can be performed – is usually missed, since it can hardly be derived by observing transitions only. In this paper we introduce ASCoL, a tool that exploits graph analysis for automatically identifying static relations, in order to enhance planning domain models. ASCoL has been evaluated on domain models generated by LOCM for international planning competition domains, and has been shown to be effective.

KW - Automated planning

KW - Domain model acquisition

KW - Knowledge engineering

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - AIIA 2015 - Advances in Artificial Intelligence - XIVth International Conference of the Italian Association for Artificial Intelligence, Proceedings

PB - Springer Verlag

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Jilani R, Crampton A, Kitchin D, Vallati M. Ascol: A tool for improving automatic planning domain model acquisition. In AIIA 2015 - Advances in Artificial Intelligence - XIVth International Conference of the Italian Association for Artificial Intelligence, Proceedings. Vol. 9336. Springer Verlag. 2015. p. 438-451. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-24309-2_33