{"type":"ScholarlyArticle","title":"Automated Generation of Understandable Contingency Plans","authors":[{"name":"Max Horstmann","slug":"max-horstmann","given":"Max","family":"Horstmann","affiliation":""},{"name":"Shlomo Zilberstein","slug":"shlomo-zilberstein","given":"Shlomo","family":"Zilberstein","affiliation":""}],"abstract":"Markov Decision Processes (MDPs) and contingency planning (CP) are two widely used approaches to planning under uncertainty. MDPs are attractive because the model is extremely general and because many algorithms exist for deriving optimal plans. In contrast, CP is normally performed using heuristic techniques that do not guarantee optimality, but the resulting plans are more compact and more understandable. The inability to present MDP policies in a clear, intuitive way has limited their applicability in some important domains. We introduce an anytime algorithm for deriving contingency plans that combines the advantages of the two approaches.","doi":null,"url":"https://ijcai.org/proceedings/2003/257","pdf":"https://ijcai.org/proceedings/2003/0257.pdf","markdown":"https://ijcai.org/proceedings/2003/257.md","bibtex":"https://ijcai.org/proceedings/2003/bibtex/257","volume":{"slug":"2003","title":"Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2003","url":"https://ijcai.org/proceedings/2003/","year":2003,"location":"Acapulco, Mexico","dates":"","editors":"Georg Gottlob","isbn":"","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":257,"pages":{"first":1518,"last":1518},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}