Integrating Learning and Reasoning with Deep Logic Models
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Combining connectionist and symbolic paradigms seems a favourable way to address the challenge of explainability in DL.
“Combining connectionist and symbolic paradigms seems a favourable way to address this challenge [169, 299, 312, 352, 353].”
From Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI (2020) · cites this paper as [353]
Cites
Learning Logical Definitions from Relations
Quinlan, J. R. (1990) · Machine Learning · doi:10.1023/a:1022699322624 · Cited by 1,644 · see citing works
Efficient Piecewise Training of Deep Structured Models for Semantic Segmentation
Lin, G., Shen, C., van den Hengel, A., & Reid, I. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.348 · Cited by 862 · see citing works
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Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., et al. (2020) · Information Fusion · doi:10.1016/j.inffus.2019.12.012 · Cited by 10,423 · see citing works
Augmenting large language models with chemistry tools
M. Bran, A., Cox, S., Schilter, O., Baldassari, C., White, A. D., & Schwaller, P. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00832-8 · Cited by 794 · see citing works
Olausson, T., Gu, A., Lipkin, B., Zhang, C., Solar-Lezama, A., Tenenbaum, J., & Levy, R. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.313 · Cited by 57 · see citing works
“…have seen broad uptake in domains adjacent to logical reasoning, such as generating outputs consistent with a pre-existing symbolic knowledge base (Marra et al., 2019; Manhaeve et al., 2018; Zhang et al., 2023a) and performing algorithmic reasoning over symbolically grounded inputs (Ebrahimi et…”
Background
- Authors
- Marra, G., Giannini, F., Diligenti, M., & Gori, M.
- Affiliations
- University of Siena (IT)
- Corresponding author
- Diligenti, Michelangelo
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2020
- Pages
- 517-532
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2908522859
- Cited by
- 71
- References
- 2
- Citations by year
- 2026: 14 · 2025: 3 · 2024: 6 · 2023: 10 · 2022: 7 · 2021: 17 · 2020: 10 · 2019: 4
- FWCI
- 4.12
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Lecture notes in computer science (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- CINECA IRIS Institutial research information system (University of Pisa) (submittedVersion)
- Scuola Normale Superiore di Pisa (submittedVersion)
- Use Siena air (University of Siena) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Logic, Reasoning, and Knowledge
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Keywords
- meta-parameters, intelligent agents
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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