Logic tensor networks for semantic image interpretation
- Submitting institution
-
City, University of London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 793
- Type
- E - Conference contribution
- DOI
-
10.24963/ijcai.2017/221
- Title of conference / published proceedings
- Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
- First page
- 1596
- Volume
- -
- Issue
- -
- ISSN
- 1045-0823
- Open access status
- Not compliant
- Month of publication
- August
- Year of publication
- 2017
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
2
- Research group(s)
-
-
- Citation count
- -
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The constraint-based approach for learning and reasoning first proposed and applied in this paper has been adopted and extended by other hybrid AI systems, including LYRICS (U. Siena), KENN (FBK Trento) and DL2 (ETH Zurich), and used in different areas ranging from reinforcement learning to knowledge engineering. The presented Logic Tensor Networks system has since been developed by more than 20 other researchers, and can be downloaded from Github.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
