License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/DagRep.11.8.11
URN: urn:nbn:de:0030-drops-157670
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2022/15767/
Go back to Dagstuhl Reports

Dong, Tiansi ; Rettinger, Achim ; Tang, Jie ; Tversky, Barbara ; van Harmelen, Frank
Weitere Beteiligte (Hrsg. etc.): Tiansi Dong and Achim Rettinger and Jie Tang and Barbara Tversky and Frank van Harmelen

Structure and Learning (Dagstuhl Seminar 21362)

pdf-format:
dagrep_v011_i008_p011_21362.pdf (3 MB)

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.

BibTeX - Entry

@Article{dong_et_al:DagRep.11.8.11,
  author =	{Dong, Tiansi and Rettinger, Achim and Tang, Jie and Tversky, Barbara and van Harmelen, Frank},
  title =	{{Structure and Learning (Dagstuhl Seminar 21362)}},
  pages =	{11--34},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2022},
  volume =	{11},
  number =	{8},
  editor =	{Dong, Tiansi and Rettinger, Achim and Tang, Jie and Tversky, Barbara and van Harmelen, Frank},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2022/15767},
  URN =		{urn:nbn:de:0030-drops-157670},
  doi =		{10.4230/DagRep.11.8.11},
  annote =	{Keywords: Knowledge graph, Machine learning, Neural-symbol unification}
}

Keywords: Knowledge graph, Machine learning, Neural-symbol unification
Collection: DagRep, Volume 11, Issue 8
Issue Date: 2022
Date of publication: 18.02.2022

DROPS-Home | Fulltext Search | Imprint | Privacy Published by LZI