{"type":"ScholarlyArticle","title":"Stochastic and-or Grammars: A Unified Framework and Logic Perspective","authors":[{"name":"Kewei Tu","slug":"kewei-tu","given":"Kewei","family":"Tu","affiliation":""}],"abstract":"Stochastic And-Or grammars (AOG) extend traditional stochastic grammars of language to model other types of data such as images and events. In this paper we propose a representation framework of stochastic AOGs that is agnostic to the type of the data being modeled and thus unifies various domain-specific AOGs. Many existing grammar formalisms and probabilistic models in natural language processing, computer vision, and machine learning can be seen as special cases of this framework. We also propose a domain-independent inference algorithm of stochastic context-free AOGs and show its tractability under a reasonable assumption. Furthermore, we provide two interpretations of stochastic context-free AOGs as a subset of probabilistic logic, which connects stochastic AOGs to the field of statistical relational learning and clarifies their relation with a few existing statistical relational models.","doi":null,"url":"https://ijcai.org/proceedings/2016/377","pdf":"https://ijcai.org/proceedings/2016/0377.pdf","markdown":"https://ijcai.org/proceedings/2016/377.md","bibtex":"https://ijcai.org/proceedings/2016/bibtex/377","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":377,"pages":{"first":2654,"last":2659},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}