{"type":"ScholarlyArticle","title":"Tractable Learning for Structured Probability Spaces: A Case Study in Learning Preference Distributions","authors":[{"name":"Arthur Choi","slug":"arthur-choi","given":"Arthur","family":"Choi","affiliation":""},{"name":"Guy Van den Broeck","slug":"guy-van-den-broeck","given":"Guy","family":"Van den Broeck","affiliation":""},{"name":"Adnan Darwiche","slug":"adnan-darwiche","given":"Adnan","family":"Darwiche","affiliation":""}],"abstract":"Probabilistic sentential decision diagrams (PSDDs) are a tractable representation of structured probability spaces, which are characterized by complex logical constraints on what constitutes a possible world. We develop general-purpose techniques for probabilistic reasoning and learning with PSDDs, allowing one to compute the probabilities of arbitrary logical formulas and to learn PSDDs from incomplete data. We illustrate the effectiveness of these techniques in the context of learning preference distributions, to which considerable work has been devoted in the past. We show, analytically and empirically, that our proposed framework is general enough to support diverse and complex data and query types. In particular, we show that it can learn maximum-likelihood models from partial rankings, pairwise preferences, and arbitrary preference constraints. Moreover, we show that it can efficiently answer many queries exactly, from expected and most likely rankings, to the probability of pairwise preferences, and diversified recommendations. This case study illustrates the effectiveness and flexibility of the developed PSDD framework as a domain-independent tool for learning and reasoning with structured probability spaces.","doi":null,"url":"https://ijcai.org/proceedings/2015/405","pdf":"https://ijcai.org/proceedings/2015/0405.pdf","markdown":"https://ijcai.org/proceedings/2015/405.md","bibtex":"https://ijcai.org/proceedings/2015/bibtex/405","volume":{"slug":"2015","title":"Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2015","url":"https://ijcai.org/proceedings/2015/","year":2015,"location":"Buenos Aires, Argentina","dates":"25–31 July 2015","editors":"Qiang Yang and Michael Wooldridge","isbn":"978-1-57735-738-4","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":"Special Track on Knowledge Representation and Reasoning","track":"Special Track on Knowledge Representation and Reasoning","number":405,"pages":{"first":2861,"last":2867},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}