Refining Process Descriptions from Execution Data in Hybrid Planning Domain Models

Alan Lindsay, Santiago Franco Aixela, Rubiya Reba, Lee McCluskey

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)


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 contribution of this work is 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
Subtitle of host publication(ICAPS 2020)
EditorsJ. Christopher Beck, Olivier Buffet, Jörg Hoffmann, Erez Karpas, Shirin Sohrabi
PublisherAAAI press
Number of pages9
ISBN (Print)9781577358244
Publication statusPublished - 29 May 2020
Event30th International Conference on Automated Planning and Scheduling - Online, France
Duration: 19 Oct 202030 Oct 2020
Conference number: 30

Publication series

NameProceedings Of The Thirtieth International Conference On Automated Planning And Scheduling
PublisherAAAI Press
ISSN (Print)2334-0835
ISSN (Electronic)2334-0843


Conference30th International Conference on Automated Planning and Scheduling
Abbreviated titleICAPS 2020
Internet address


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