{"type":"ScholarlyArticle","title":"Systems AI: A Declarative Learning Based Programming Perspective","authors":[{"name":"Parisa Kordjamshidi","slug":"parisa-kordjamshidi","given":"Parisa","family":"Kordjamshidi","affiliation":"a Tulane University , Dept. of Computer Science , USA; b Florida Institute for Human and Machine Cognition , USA"},{"name":"Dan Roth","slug":"dan-roth","given":"Dan","family":"Roth","affiliation":"c University of Pennsylvania , Dept. of Computer and Information Science , USA"},{"name":"Kristian Kersting","slug":"kristian-kersting","given":"Kristian","family":"Kersting","affiliation":"d TU Darmstadt , Computer Science Dept. & Centre for Cognitive Science , Germany"}],"abstract":"Data-driven approaches are becoming dominant problem-solving techniques in many areas of research and industry. Unfortunately, current technologies do not make such techniques easy to use for application experts who are not fluent in machine learning nor for machine learning experts who aim at testing ideas on real-world data and need to evaluate those as a part of an end-to-end system. \r\nWe review key efforts made by various AI communities to provide languages for high-level abstractions over learning and reasoning techniques needed for designing complex AI systems. We classify the existing frameworks based on the type of techniques as well as the data and knowledge representations they use, provide a comparative study of the way they address the challenges of programming real-world applications, and highlight some shortcomings and future directions.","doi":"10.24963/ijcai.2018/771","url":"https://ijcai.org/proceedings/2018/771","pdf":"https://ijcai.org/proceedings/2018/0771.pdf","markdown":"https://ijcai.org/proceedings/2018/771.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/771","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Survey track","track":"Survey track","number":771,"pages":{"first":5464,"last":5471},"keywords":["Machine Learning: Machine Learning","Machine Learning: Relational Learning"],"videos":[],"references":[{"position":1,"raw":"[Abadi et al., 2016] Martin Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, Manjunath Kudlur, Josh Levenberg, Rajat Monga, Sherry Moore, Derek Gordon Murray, Benoit Steiner, Paul A. 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