{"type":"ScholarlyArticle","title":"Probabilistic Goal Markov Decision Processes","authors":[{"name":"Huan Xu","slug":"huan-xu","given":"Huan","family":"Xu","affiliation":""},{"name":"Shie Mannor","slug":"shie-mannor","given":"Shie","family":"Mannor","affiliation":""}],"abstract":"The Markov decision process model is a powerful tool in planing tasks and sequential decision making problems. The randomness of state transitions and rewards implies that the performance of a policy is often stochastic. In contrast to the standard approach that studies the expected performance, we consider the policy that maximizes the probability of achieving a pre-determined target performance, a criterion we term *probabilistic goal Markov decision processes*. We show that this problem is NP-hard, but can be solved using a pseudo-polynomial algorithm. We further consider a variant dubbed chance-constraint Markov decision problems, that treats the probability of achieving target performance as a constraint instead of the maximizing objective. This variant is NP-hard, but can be solved in pseudo-polynomial time.","doi":"10.5591/978-1-57735-516-8/IJCAI11-341","url":"https://ijcai.org/proceedings/2011/341","pdf":"https://ijcai.org/proceedings/2011/0341.pdf","markdown":"https://ijcai.org/proceedings/2011/341.md","bibtex":"https://ijcai.org/proceedings/2011/bibtex/341","volume":{"slug":"2011","title":"Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2011","url":"https://ijcai.org/proceedings/2011/","year":2011,"location":"Barcelona, Catalonia, Spain","dates":"16–22 July 2011","editors":"Toby Walsh","isbn":"978-1-57735-512-0; 978-1-57735-513-7; 978-1-57735-514-4; 978-1-57735-515-1; 978-1-57735-516-8","publisher":"AAAI Press/International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":341,"pages":{"first":2046,"last":2051},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}