{"type":"ScholarlyArticle","title":"Intelligent Belief State Sampling for Conformant Planning","authors":[{"name":"Alban Grastien","slug":"alban-grastien","given":"Alban","family":"Grastien","affiliation":"The Australian National University , Canberra , Australia; Data61 , Canberra , Australia"},{"name":"Enrico Scala","slug":"enrico-scala","given":"Enrico","family":"Scala","affiliation":"The Australian National University , Canberra , Australia; Fondazione Bruno Kessler , Trento , Italy"}],"abstract":"We propose a new method for conformant planning based on two ideas.  First given a small sample of the initial belief state  we reduce conformant planning for this sample  to a classical planning problem, giving us a candidate solution.  Second we exploit regression as a way to compactly represent  necessary conditions for such a solution  to be valid for the non-deterministic setting.   If necessary, we use the resulting formula  to extract a counter-example to populate our next sampling.  Our experiments show that this approach  is competitive on a class of problems  that are hard for traditional planners,   and also returns generally shorter plans.  We are also able to demonstrate unsatisfiability of some problems.","doi":"10.24963/ijcai.2017/603","url":"https://ijcai.org/proceedings/2017/603","pdf":"https://ijcai.org/proceedings/2017/0603.pdf","markdown":"https://ijcai.org/proceedings/2017/603.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/603","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Planning and Scheduling","track":"Main track","number":603,"pages":{"first":4317,"last":4323},"keywords":["Planning and Scheduling: Conformant/Contingent planning","Constraints and Satisfiability: Satisfiability"],"videos":[],"references":[{"position":1,"raw":"[Albore et al., 2010] A. 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