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Engineering Knowledge for Automated Planning: Towards a Notion of Quality

McCluskey, T.L., Vaquero, Tiago and Vallati, Mauro (2017) Engineering Knowledge for Automated Planning: Towards a Notion of Quality. In: K-CAP 2017, December 4th - 6th, 2017, Austin, Texas. (In Press)

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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, focussing 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.

Item Type: Conference or Workshop Item (Paper)
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Schools: School of Computing and Engineering
Related URLs:
Depositing User: Lee Mccluskey
Date Deposited: 11 Oct 2017 13:55
Last Modified: 12 Oct 2017 06:41
URI: http://eprints.hud.ac.uk/id/eprint/33581

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