Engineering Knowledge for Automated Planning: Towards a Notion of Quality

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Automated planning is a prominent Artificial Intelligence challenge, as well as being a common capability requirement for intelligent autonomous agents. A critical aspect of what is called domain-independent planning, is the application knowledge that must be added to the planner to create a complete planning application. This is made explicit in (i) a domain model, which is a formal representation of the persistent domain knowledge, and (ii) an associated problem instance, containing the details of the particular problem to be solved. Both these components are used by automated planning engines for reasoning, in order to synthesize a solution plan.
Formulating knowledge for use in planning engines is currently something of an ad-hoc process, where the skills of knowledge engineers significantly influence the quality of the resulting planning application. On top of that, a notion of quality of the knowledge captured within a domain model is missing; it is therefore hard to provide useful guidelines to knowledge engineers.
This paper raises some issues relating to the engineering of application knowledge for automated planning, focusing on the domain model. It uses the idea of a domain model as a formal specification of a domain, and considers what it means to measure the quality of such a specification. To do this it proposes definitions of the attributes of a domain model and its encoding language, which are needed by the automated planning community in order to improve tools for supporting the engineering of planning knowledge, and to advance toward a shared and inclusive definition of quality of domain models.
LanguageEnglish
Title of host publicationProceedings of the 9th International Conference on Knowledge Capture (K-CAP), (Austin, TX: 4-6 December 2017)
PublisherAssociation for Computing Machinery (ACM)
Number of pages8
ISBN (Print)9781450355537
DOIs
Publication statusPublished - 4 Dec 2017
Event9th International Conference on Knowledge Capture - Hilton Garden Inn Convention Center, Austin, United States
Duration: 4 Dec 20176 Dec 2017
Conference number: 9
https://k-cap2017.org/ (Link to Conference Website)

Conference

Conference9th International Conference on Knowledge Capture
Abbreviated titleK-CAP 2017
CountryUnited States
CityAustin
Period4/12/176/12/17
Internet address

Fingerprint

Knowledge engineering
Planning
Engines
Engineers
Autonomous agents
Artificial intelligence
Specifications

Cite this

McCluskey, T., Vaquero, T., & Vallati, M. (2017). Engineering Knowledge for Automated Planning: Towards a Notion of Quality. In Proceedings of the 9th International Conference on Knowledge Capture (K-CAP), (Austin, TX: 4-6 December 2017) [14] Association for Computing Machinery (ACM). https://doi.org/10.1145/3148011.3148012
McCluskey, Thomas ; Vaquero, Tiago ; Vallati, Mauro. / Engineering Knowledge for Automated Planning : Towards a Notion of Quality. Proceedings of the 9th International Conference on Knowledge Capture (K-CAP), (Austin, TX: 4-6 December 2017). Association for Computing Machinery (ACM), 2017.
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McCluskey, T, Vaquero, T & Vallati, M 2017, Engineering Knowledge for Automated Planning: Towards a Notion of Quality. in Proceedings of the 9th International Conference on Knowledge Capture (K-CAP), (Austin, TX: 4-6 December 2017)., 14, Association for Computing Machinery (ACM), 9th International Conference on Knowledge Capture, Austin, United States, 4/12/17. https://doi.org/10.1145/3148011.3148012

Engineering Knowledge for Automated Planning : Towards a Notion of Quality. / McCluskey, Thomas; Vaquero, Tiago; Vallati, Mauro.

Proceedings of the 9th International Conference on Knowledge Capture (K-CAP), (Austin, TX: 4-6 December 2017). Association for Computing Machinery (ACM), 2017. 14.

Research output: Chapter in Book/Report/Conference proceedingChapter

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T2 - Towards a Notion of Quality

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AU - Vaquero, Tiago

AU - Vallati, Mauro

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N2 - Automated planning is a prominent Artificial Intelligence challenge, as well as being a common capability requirement for intelligent autonomous agents. A critical aspect of what is called domain-independent planning, is the application knowledge that must be added to the planner to create a complete planning application. This is made explicit in (i) a domain model, which is a formal representation of the persistent domain knowledge, and (ii) an associated problem instance, containing the details of the particular problem to be solved. Both these components are used by automated planning engines for reasoning, in order to synthesize a solution plan.Formulating knowledge for use in planning engines is currently something of an ad-hoc process, where the skills of knowledge engineers significantly influence the quality of the resulting planning application. On top of that, a notion of quality of the knowledge captured within a domain model is missing; it is therefore hard to provide useful guidelines to knowledge engineers.This paper raises some issues relating to the engineering of application knowledge for automated planning, focusing on the domain model. It uses the idea of a domain model as a formal specification of a domain, and considers what it means to measure the quality of such a specification. To do this it proposes definitions of the attributes of a domain model and its encoding language, which are needed by the automated planning community in order to improve tools for supporting the engineering of planning knowledge, and to advance toward a shared and inclusive definition of quality of domain models.

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BT - Proceedings of the 9th International Conference on Knowledge Capture (K-CAP), (Austin, TX: 4-6 December 2017)

PB - Association for Computing Machinery (ACM)

ER -

McCluskey T, Vaquero T, Vallati M. Engineering Knowledge for Automated Planning: Towards a Notion of Quality. In Proceedings of the 9th International Conference on Knowledge Capture (K-CAP), (Austin, TX: 4-6 December 2017). Association for Computing Machinery (ACM). 2017. 14 https://doi.org/10.1145/3148011.3148012