{"type":"ScholarlyArticle","title":"A POMDP Approach to Influence Diagram Evaluation","authors":[{"name":"Eric A. Hansen","slug":"eric-a-hansen","given":"Eric A.","family":"Hansen","affiliation":""},{"name":"Jinchuan Shi","slug":"jinchuan-shi","given":"Jinchuan","family":"Shi","affiliation":""},{"name":"Arindam Khaled","slug":"arindam-khaled","given":"Arindam","family":"Khaled","affiliation":""}],"abstract":"We propose a node-removal/arc-reversal algorithm for influence diagram evaluation that includes reductions that allow an influence diagram to be solved by a generalization of the dynamic programming approach to solving partially observable Markov decision processes (POMDPs). Among its potential advantages, the algorithm allows a more flexible ordering of node removals, and a POMDP-inspired approach to optimizing over hidden state variables, which can improve the scalability of influence diagram evaluation in solving complex, multi-stage problems. It also finds a more compact representation of an optimal strategy.","doi":null,"url":"https://ijcai.org/proceedings/2016/443","pdf":"https://ijcai.org/proceedings/2016/0443.pdf","markdown":"https://ijcai.org/proceedings/2016/443.md","bibtex":"https://ijcai.org/proceedings/2016/bibtex/443","volume":{"slug":"2016","title":"Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2016","url":"https://ijcai.org/proceedings/2016/","year":2016,"location":"New York, NY, USA","dates":"9–15 July 2016","editors":"Subbarao Kambhampati","isbn":"978-1-57735-770-4; 978-1-57735-771-1","publisher":"AAAI Press / International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":443,"pages":{"first":3124,"last":3131},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}