A Logic-based Explanation Generation Framework for Classical and Hybrid Planning Problems (Extended Abstract)

  • Stlylianos Loukas Vasileiou
  • , William Yeoh
  • , Son Tran
  • , Ashwin Kumar
  • , Michael Cashmore
  • , Daniele Magazzeni

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

Abstract

In human-aware planning systems, a planning agent might need to explain its plan to a human user when that plan appears to be non-feasible or sub-optimal. A popular approach, called model reconciliation, has been proposed as a way to bring the model of the human user closer to the agent's model. In this paper, we approach the model reconciliation problem from a different perspective, that of knowledge representation and reasoning, and demonstrate that our approach can be applied not only to classical planning problems but also hybrid systems planning problems with durative actions and events/processes.

Original languageEnglish
Title of host publicationProceedings of the 32nd International Joint Conference on Artificial Intelligence, IJCAI 2023
EditorsEdith Elkind
PublisherInternational Joint Conferences on Artificial Intelligence
Pages6985-6989
Number of pages5
ISBN (Electronic)9781956792034
DOIs
StatePublished - 2023
Event32nd International Joint Conference on Artificial Intelligence, IJCAI 2023 - Macao, China
Duration: Aug 19 2023Aug 25 2023

Publication series

NameIJCAI International Joint Conference on Artificial Intelligence
Volume2023-August
ISSN (Print)1045-0823

Conference

Conference32nd International Joint Conference on Artificial Intelligence, IJCAI 2023
Country/TerritoryChina
CityMacao
Period08/19/2308/25/23

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