{"type":"ScholarlyArticle","title":"Decreasing Uncertainty in Planning with State Prediction","authors":[{"name":"Senka Krivic","slug":"senka-krivic","given":"Senka","family":"Krivic","affiliation":"Department of Computer Science , University of Innsbruck , Austria"},{"name":"Michael Cashmore","slug":"michael-cashmore","given":"Michael","family":"Cashmore","affiliation":"Department of Computer Science , King's College London , United Kingdom"},{"name":"Daniele Magazzeni","slug":"daniele-magazzeni","given":"Daniele","family":"Magazzeni","affiliation":"Department of Computer Science , King's College London , United Kingdom"},{"name":"Bram Ridder","slug":"bram-ridder","given":"Bram","family":"Ridder","affiliation":"Department of Computer Science , King's College London , United Kingdom"},{"name":"Sandor Szedmak","slug":"sandor-szedmak","given":"Sandor","family":"Szedmak","affiliation":"Department of Computer Science , Aalto University , Finland"},{"name":"Justus Piater","slug":"justus-piater","given":"Justus","family":"Piater","affiliation":"Department of Computer Science , University of Innsbruck , Austria"}],"abstract":"In real world environments the state is almost never completely known. Exploration is often expensive. The application of planning in these environments is consequently more difficult and less robust. In this paper we present an approach for predicting new information about a partially-known state. The state is translated into a partially-known multigraph, which can then be extended using machine-learning techniques. We demonstrate the effectiveness of our approach, showing that it enhances the scalability of our planners, and leads to less time spent on sensing actions.","doi":"10.24963/ijcai.2017/282","url":"https://ijcai.org/proceedings/2017/282","pdf":"https://ijcai.org/proceedings/2017/0282.pdf","markdown":"https://ijcai.org/proceedings/2017/282.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/282","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":"Machine Learning A-R","track":"Main track","number":282,"pages":{"first":2032,"last":2038},"keywords":["Uncertainty in AI: Uncertainty in AI","Planning and Scheduling: Robot Planning","Planning and Scheduling: Planning under Uncertainty","Machine Learning: Semi-Supervised Learning"],"videos":[],"references":[{"position":1,"raw":"[Albore and Geffner, 2009] H.; Albore, A.; Palacios and H. 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