DagRep.11.8.11.pdf
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Structure and Learning (Dagstuhl Seminar 21362)
Authors and all authors of the abstracts in this report
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Dagstuhl Reports, Volume 11, Issue 8
Volume: Dagstuhl Reports, Volume 11
Journal: Dagstuhl Reports (DagRep) - License:
Creative Commons Attribution 4.0 International license
- Publication Date: 2022-02-18
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Tiansi Dong, Achim Rettinger, Jie Tang, Barbara Tversky, and Frank van Harmelen. Structure and Learning (Dagstuhl Seminar 21362). In Dagstuhl Reports, Volume 11, Issue 8, pp. 11-34, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)
https://doi.org/10.4230/DagRep.11.8.11
https://doi.org/10.4230/DagRep.11.8.11
Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 21362 "Structure and Learning", held from September 5 to 10, 2021. Structure and learning are among the most prominent topics in Artificial Intelligence (AI) today. Integrating symbolic and numeric inference was set as one of the next open AI problems at the Townhall meeting "A 20 Year Roadmap for AI" at AAAI 2019. In this Dagstuhl seminar, we discussed related problems from an interdiscplinary perspective, in particular, Cognitive Science, Cognitive Psychology, Physics, Computational Humor, Linguistic, Machine Learning, and AI. This report overviews presentations and working groups during the seminar, and lists two open problems.
Subject Classification
ACM Subject Classification
- Computing methodologies → Artificial intelligence
- Computing methodologies → Machine learning
- Computing methodologies → Symbolic and algebraic manipulation
Keywords
- Knowledge graph
- Machine learning
- Neural-symbol unification
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