{"type":"ScholarlyArticle","title":"A Logic-based Explanation Generation Framework for Classical and Hybrid Planning Problems (Extended Abstract)","authors":[{"name":"Stylianos Loukas Vasileiou","slug":"stylianos-loukas-vasileiou","given":"Stylianos Loukas","family":"Vasileiou","affiliation":"Washington University in St. Louis"},{"name":"William Yeoh","slug":"william-yeoh","given":"William","family":"Yeoh","affiliation":"Washington University in St. Louis"},{"name":"Son Tran","slug":"son-tran","given":"Son","family":"Tran","affiliation":"New Mexico State University"},{"name":"Ashwin Kumar","slug":"ashwin-kumar","given":"Ashwin","family":"Kumar","affiliation":"Washington University in St. Louis"},{"name":"Michael Cashmore","slug":"michael-cashmore","given":"Michael","family":"Cashmore","affiliation":"University of Strathclyde"},{"name":"Daniele Magazzeni","slug":"daniele-magazzeni","given":"Daniele","family":"Magazzeni","affiliation":"King's College London"}],"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.","doi":"10.24963/ijcai.2023/795","url":"https://ijcai.org/proceedings/2023/795","pdf":"https://ijcai.org/proceedings/2023/0795.pdf","markdown":"https://ijcai.org/proceedings/2023/795.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/795","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Journal Track","track":"Journal Track","number":795,"pages":{"first":6985,"last":6989},"keywords":["Planning and Scheduling: PS: Model-based reasoning","Knowledge Representation and Reasoning: KRR: Belief change","Knowledge Representation and Reasoning: KRR: Diagnosis and abductive reasoning","Humans and AI: HAI: Human-AI collaboration"],"videos":[],"references":[{"position":1,"raw":"[Cashmore et al., 2016] Michael Cashmore, Maria Fox, Derek Long, and Daniele Magazzeni. 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