Refining Process Descriptions from Execution Data in Hybrid Planning Domain Models

Alan Lindsay, Santiago Franco, Rubiya Reba, Thomas L. McCluskey

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

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 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
PublisherAAAI Press
Pages469-477
Number of pages9
Publication statusPublished - 1 Jun 2020
Event30th International Conference on Automated Planning and Scheduling 2020 - Nancy, France
Duration: 26 Oct 202030 Oct 2020
Conference number: 30
https://icaps20.icaps-conference.org/

Publication series

NameProceedings of the International Conference on Automated Planning and Scheduling
Volume30
ISSN (Electronic)2334-0843

Conference

Conference30th International Conference on Automated Planning and Scheduling 2020
Abbreviated titleICAPS
CountryFrance
CityNancy
Period26/10/2030/10/20
Internet address

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Information Systems and Management

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  • Cite this

    Lindsay, A., Franco, S., Reba, R., & McCluskey, T. L. (2020). Refining Process Descriptions from Execution Data in Hybrid Planning Domain Models. In Proceedings of the Thirtieth International Conference on Automated Planning and Scheduling (pp. 469-477). (Proceedings of the International Conference on Automated Planning and Scheduling; Vol. 30). AAAI Press. https://aaai.org/ojs/index.php/ICAPS/article/view/6742