{"type":"ScholarlyArticle","title":"Conditional Planning in the Discrete Belief Space","authors":[{"name":"Jussi Rintanen","slug":"jussi-rintanen","given":"Jussi","family":"Rintanen","affiliation":""}],"abstract":"Probabilistic planning with observability restrictions, as formalized for example as partially observable Markov decision processes (POMDP), has a wide range of applications, but it is computationally extremely difficult. For POMDPs, the most general decision problems about existence of policies satisfying certain properties are undecidable. We consider a computationally easier form of planning that ignores exact probabilities, and give an algorithm for a class of planning problems with partial observability. We show that the basic backup step in the algorithm is NP-complete. Then we proceed to give an algorithm for the backup step, and demonstrate how it can be used as a basis of an efficient algorithm for constructing plans.","doi":null,"url":"https://ijcai.org/proceedings/2005/128","pdf":"https://ijcai.org/proceedings/2005/0128.pdf","markdown":"https://ijcai.org/proceedings/2005/128.md","bibtex":"https://ijcai.org/proceedings/2005/bibtex/128","volume":{"slug":"2005","title":"Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2005","url":"https://ijcai.org/proceedings/2005/","year":2005,"location":"Edinburgh, Scotland, UK","dates":"","editors":"","isbn":"","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":128,"pages":{"first":1260,"last":1264},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}