Refining Process Descriptions from Execution Data in Hybrid Planning Domain Models. / Linday, Alan; Franco Aixela, Santiago; Reya, Rubiya; McCluskey, Lee.

Proceedings of the Thirtieth International Conference on Automated Planning and Scheduling, Nancy, France, October 26-30, 2020. Vol. 30 (2020) AAAI Press, 2020. p. 469-477.

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Abstract

The creation and maintenance of a domain model is a well recognised bottleneck in the use of automated planning; indeed, ensuring a planning engine is fed with an accurate model of an application is essential in order that generated plans are effective. Engineering domain models using a hybrid representation is particularly challenging as it requires accurately describing continuous processes, which can have complex numeric effects. In this work we consider the problem of the refinement of an engineered hybrid domain model, to more accurately capture the effect of the underlying processes. Our approach exploits the information content of the original model, utilising machine learning techniques to identify important situation and temporal features that indicate a variation in the original effect. We use the problem of modelling traffic flows in an Urban Traffic Management setting as a case study and demonstrate in our evaluation that the refined domain models provide more accurate simulation, which can lead to higher quality plans. The contributions of this work are a general approach to the automated refinement of hybrid planning domain models that reduces the knowledge engineering effort in producing a detailed process model. The approach can be used for refining the domain model during the initial stages of development, or for re-configuring the domain model when used in the same problem area but with a different scenario. We test out the approach within a real world case study.
Original languageEnglish
Title of host publicationProceedings of the Thirtieth International Conference on Automated Planning and Scheduling, Nancy, France, October 26-30, 2020
PublisherAAAI Press
Pages469-477
Number of pages9
Volume30 (2020)
ISBN (Electronic)978-1-57735-824-4
Publication statusPublished - 1 Jun 2020
This open access research output is licenced under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.

ID: 36377320