{"type":"ScholarlyArticle","title":"NeurASP: Embracing Neural Networks into Answer Set Programming","authors":[{"name":"Zhun Yang","slug":"zhun-yang","given":"Zhun","family":"Yang","affiliation":"Arizona State University , Tempe , AZ , USA"},{"name":"Adam Ishay","slug":"adam-ishay","given":"Adam","family":"Ishay","affiliation":"Arizona State University , Tempe , AZ , USA"},{"name":"Joohyung Lee","slug":"joohyung-lee","given":"Joohyung","family":"Lee","affiliation":"Arizona State University , Tempe , AZ , USA; Samsung Research , Seoul , South Korea"}],"abstract":"We present NeurASP, a simple extension of answer set programs by embracing neural networks. By treating the neural network output as the probability distribution over atomic facts in answer set programs, NeurASP provides a simple and effective way to integrate sub-symbolic and symbolic computation. We demonstrate how NeurASP can make use of a pre-trained neural network in symbolic computation and how it can improve the neural network's perception result by applying symbolic reasoning in answer set programming.  Also, NeurASP can make use of ASP rules to train a neural network better so that a neural network not only learns from implicit correlations from the data but also from the explicit complex semantic constraints expressed by the rules.","doi":"10.24963/ijcai.2020/243","url":"https://ijcai.org/proceedings/2020/243","pdf":"https://ijcai.org/proceedings/2020/0243.pdf","markdown":"https://ijcai.org/proceedings/2020/243.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/243","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":"Knowledge Representation and Reasoning","track":"Main track","number":243,"pages":{"first":1755,"last":1762},"keywords":["Machine Learning: Neuro-Symbolic Methods","Knowledge Representation and Reasoning: Knowledge Representation Languages"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/25328/file_25328.m3u8","length_sec":301,"provider":"hls"}],"references":[{"position":1,"raw":"[Adam et al., 2017] Paszke Adam, Gross Sam, Chintala Soumith, Chanan Gregory, Yang Edward, D Zachary, Lin Zeming, Desmaison Alban, Antiga Luca, and Lerer Adam. 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