Towards the integration of model predictive control into an AI planning framework

Falilat Jimoh, Thomas L. McCluskey

Research output: Contribution to journalConference article

1 Citation (Scopus)

Abstract

This paper describes a framework for a hybrid algorithm that combines both AI Planning and Model Predictive Control approaches to reason with processes and events within a domain. This effectively utilises the strengths of search-based and model-simulation-based methods. We explore this control approach and show how it can be embedded into existing, modern AI Planning technology. This preserves the many advantages of the AI Planning approach, to do with domain independence through declarative modelling, and explicit reasoning, while leveraging the capability of MPC to deal with continuous processes computation within such domains. The developed technique is tested on an urban traffic control application and the results demonstrate the potential in utilising MPC as a heurisic to guide planning search.

LanguageEnglish
Number of pages8
JournalCEUR Workshop Proceedings
Volume1782
Publication statusPublished - 16 Jan 2017
Event34th Workshop of the UK Planning and Scheduling Special Interest Group - University of Huddersfield, Huddersfield, United Kingdom
Duration: 15 Dec 201616 Dec 2016
Conference number: 34
https://plansig2016.wordpress.com/ (Link to Event Website)

Fingerprint

Model predictive control
Planning
Traffic control

Cite this

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Towards the integration of model predictive control into an AI planning framework. / Jimoh, Falilat; McCluskey, Thomas L.

In: CEUR Workshop Proceedings, Vol. 1782, 16.01.2017.

Research output: Contribution to journalConference article

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