{"type":"ScholarlyArticle","title":"Scaling Up AND/OR Abstraction Sampling","authors":[{"name":"Kalev Kask","slug":"kalev-kask","given":"Kalev","family":"Kask","affiliation":"University of California , Irvine , Irvine , CA 92697 , USA"},{"name":"Bobak Pezeshki","slug":"bobak-pezeshki","given":"Bobak","family":"Pezeshki","affiliation":"University of California , Irvine , Irvine , CA 92697 , USA"},{"name":"Filjor Broka","slug":"filjor-broka","given":"Filjor","family":"Broka","affiliation":"University of California , Irvine , Irvine , CA 92697 , USA"},{"name":"Alexander Ihler","slug":"alexander-ihler","given":"Alexander","family":"Ihler","affiliation":"University of California , Irvine , Irvine , CA 92697 , USA"},{"name":"Rina Dechter","slug":"rina-dechter","given":"Rina","family":"Dechter","affiliation":"University of California , Irvine , Irvine , CA 92697 , USA"}],"abstract":"Abstraction Sampling (AS) is a recently introduced enhancement of Importance Sampling that exploits stratification by using a notion of abstractions: groupings of similar nodes into abstract states. It was previously shown that AS performs particularly well when sampling over an AND/OR search space; however, existing schemes were limited to ``proper'' abstractions in order to ensure unbiasedness, severely hindering scalability. In this paper, we introduce AOAS, a new Abstraction Sampling scheme on AND/OR search spaces that allow more flexible use of abstractions by circumventing the properness requirement. We analyze the properties of this new algorithm and, in an extensive empirical evaluation on five benchmarks, over 480 problems, and comparing against other state of the art algorithms, illustrate AOAS's properties and show that it provides a far more powerful and competitive Abstraction Sampling framework.","doi":"10.24963/ijcai.2020/589","url":"https://ijcai.org/proceedings/2020/589","pdf":"https://ijcai.org/proceedings/2020/0589.pdf","markdown":"https://ijcai.org/proceedings/2020/589.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/589","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Uncertainty in AI","track":"Main track","number":589,"pages":{"first":4266,"last":4274},"keywords":["Uncertainty in AI: Approximate Probabilistic Inference","Uncertainty in AI: Graphical Models","Uncertainty in AI: Bayesian Networks"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/26978/file_26978.m3u8","length_sec":298,"provider":"hls"}],"references":[{"position":1,"raw":"[Bidyuk and Dechter, 2007] Bozhena Bidyuk and Rina Dechter. 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