{"type":"ScholarlyArticle","title":"A Brief History of Learning Symbolic Higher-Level Representations from Data (And a Curious Look Forward)","authors":[{"name":"Stefan Kramer","slug":"stefan-kramer","given":"Stefan","family":"Kramer","affiliation":""}],"abstract":"Learning higher-level representations from data has been on the agenda of AI research for several decades. In the paper, I will give a survey of various approaches to learning symbolic higher-level representations: feature construction and constructive induction, predicate invention, propositionalization, pattern mining, and mining time series patterns. Finally, I will give an outlook on how approaches to learning higher-level representations, symbolic and neural, can benefit from each other to solve current issues in machine learning.","doi":"10.24963/ijcai.2020/678","url":"https://ijcai.org/proceedings/2020/678","pdf":"https://ijcai.org/proceedings/2020/0678.pdf","markdown":"https://ijcai.org/proceedings/2020/678.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/678","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":"Survey track","track":"Survey track","number":678,"pages":{"first":4868,"last":4876},"keywords":["Knowledge Representation and Reasoning: general","Machine Learning: general"],"videos":[],"references":[{"position":1,"raw":"[Agrawal & Srikant, 1994] R. 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