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February 18, 2022
Journal Article
Title

Structure and Learning

Title Supplement
Report from Dagstuhl Seminar 21362
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.
Author(s)
Dong, Tiansi
Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS  
Rettinger, Achim
Tang, Jie
Tsinghua University, Beijing, China
Tversky, Barbara
Columbia University, New York, USA
Harmelen, Frank van
VU University Amsterdam, NL
Journal
Dagstuhl Reports  
Open Access
File(s)
Download (3.06 MB)
Rights
CC BY 4.0: Creative Commons Attribution
DOI
10.4230/DagRep.11.8.11
10.24406/publica-1963
Language
English
Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS  
Keyword(s)
  • Knowledge graph

  • Machine learning

  • Neural-symbol unification

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