{"type":"ScholarlyArticle","title":"DeepPSL: End-to-End Perception and Reasoning","authors":[{"name":"Sridhar Dasaratha","slug":"sridhar-dasaratha","given":"Sridhar","family":"Dasaratha","affiliation":"Global Delivery Services India"},{"name":"Sai Akhil Puranam","slug":"sai-akhil-puranam","given":"Sai Akhil","family":"Puranam","affiliation":"Global Delivery Services India"},{"name":"Karmvir Singh Phogat","slug":"karmvir-singh-phogat","given":"Karmvir Singh","family":"Phogat","affiliation":"Global Delivery Services India"},{"name":"Sunil Reddy Tiyyagura","slug":"sunil-reddy-tiyyagura","given":"Sunil Reddy","family":"Tiyyagura","affiliation":"Global Delivery Services India"},{"name":"Nigel P. Duffy","slug":"nigel-p-duffy","given":"Nigel P.","family":"Duffy","affiliation":"Ernst & Young (EY) LLP USA"}],"abstract":"We introduce DeepPSL a variant of probabilistic soft logic (PSL) to produce an end-to-end trainable system that integrates reasoning and perception. PSL represents first-order logic in terms of a convex graphical model – hinge-loss Markov random fields (HL-MRFs). PSL stands out among probabilistic logic frameworks due to its tractability having been applied to systems of more than 1 billion ground rules. The key to our approach is to represent predicates in first-order logic using deep neural networks and then to approximately back-propagate through the HL-MRF and thus train every aspect of the first-order system being represented. We believe that this approach represents an interesting direction for the integration of deep learning and reasoning techniques with applications to knowledge base learning, multi-task learning, and explainability. Evaluation on three different tasks demonstrates that DeepPSL significantly outperforms state-of-the-art neuro-symbolic methods on scalability while achieving comparable or better accuracy.","doi":"10.24963/ijcai.2023/401","url":"https://ijcai.org/proceedings/2023/401","pdf":"https://ijcai.org/proceedings/2023/0401.pdf","markdown":"https://ijcai.org/proceedings/2023/401.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/401","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Machine Learning","track":"Main Track","number":401,"pages":{"first":3606,"last":3614},"keywords":["Machine Learning: ML: Learning graphical models","Machine Learning: ML: Knowledge-aided learning","Machine Learning: ML: Neuro-symbolic methods"],"videos":[],"references":[{"position":1,"raw":"[Agrawal et al., 2019] Akshay Agrawal, Brandon Amos, Shane Barratt, Stephen Boyd, Steven Diamond, and J. 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