Structure and Learning (Dagstuhl Seminar 21362)

Authors Tiansi Dong, Achim Rettinger, Jie Tang, Barbara Tversky, Frank van Harmelen and all authors of the abstracts in this report

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Author Details

Tiansi Dong
  • Universität Bonn, DE
Achim Rettinger
  • Universität Trier, DE
Jie Tang
  • Tsinghua University - Beijing, CN
Barbara Tversky
  • Columbia University - New York, US
Frank van Harmelen
  • VU University Amsterdam, NL
and all authors of the abstracts in this report

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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)


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
  • Knowledge graph
  • Machine learning
  • Neural-symbol unification


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