Structure and Learning
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.
- Published in:
Dagstuhl Reports - Type:
Article - Authors:
T. Dong, A. Rettinger, J. Tang, B. Tversky, F. van Harmelen - Year:
2021
Citation information
T. Dong, A. Rettinger, J. Tang, B. Tversky, F. van Harmelen: Structure and Learning, Dagstuhl Reports, 2021, 11, 8, 11-34, https://doi.org/10.4230/DagRep.11.8.11, Dong.etal.2021,
@Article{Dong.etal.2021,
author={T. Dong, A. Rettinger, J. Tang, B. Tversky, F. van Harmelen},
title={Structure and Learning},
journal={Dagstuhl Reports},
volume={11},
number={8},
pages={11-34},
url={https://doi.org/10.4230/DagRep.11.8.11},
year={2021},
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...}}