Logic and Neural Networks (Dagstuhl Seminar 25061). Belle, V., Benedikt, M., Drachsler-Cohen, D., Neider, D., & Yuviler, T. Technical Report Schloss Dagstuhl – Leibniz-Zentrum für Informatik, 2025. Artwork Size: 20 pages, 2350077 bytes Issue: 2 Medium: application/pdf Publication Title: Dagstuhl Reports (DagRep) Volume: 15![Logic and Neural Networks (Dagstuhl Seminar 25061) [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Logic and learning are central to Computer Science, and in particular to AI-related research. Already Alan Turing envisioned in his 1950 “Computing Machinery and Intelligence” paper a combination of statistical (ab initio) machine learning and an “unemotional” symbolic language such as logic. The combination of logic and learning has received new impetus from the spectacular success of deep learning systems.
@techreport{belle_logic_2025,
title = {Logic and {Neural} {Networks} ({Dagstuhl} {Seminar} 25061)},
copyright = {Creative Commons Attribution 4.0 International license, info:eu-repo/semantics/openAccess},
issn = {2192-5283},
url = {https://drops.dagstuhl.de/entities/document/10.4230/DagRep.15.2.1},
doi = {10.4230/DAGREP.15.2.1},
abstract = {Logic and learning are central to Computer Science, and in particular to AI-related research. Already Alan Turing envisioned in his 1950 “Computing Machinery and Intelligence” paper a combination of statistical (ab initio) machine learning and an “unemotional” symbolic language such as logic. The combination of logic and learning has received new impetus from the spectacular success of deep learning systems.},
language = {en},
urldate = {2025-10-10},
institution = {Schloss Dagstuhl – Leibniz-Zentrum für Informatik},
author = {Belle, Vaishak and Benedikt, Michael and Drachsler-Cohen, Dana and Neider, Daniel and Yuviler, Tom},
year = {2025},
note = {Artwork Size: 20 pages, 2350077 bytes
Issue: 2
Medium: application/pdf
Publication Title: Dagstuhl Reports (DagRep)
Volume: 15},
keywords = {Computing methodologies → Artificial intelligence, Computing methodologies → Logical and relational learning, Computing methodologies → Machine learning approaches, General and reference → Surveys and overviews, Theory of computation → Constraint and logic programming, Theory of computation → Modal and temporal logics, Theory of computation → Models of learning, computational complexity, databases, learning theory, logic, machine learning, safety, verification},
pages = {1--20},
}
Downloads: 0
{"_id":"4SR4tnxLBRE9Dhc8x","bibbaseid":"belle-benedikt-drachslercohen-neider-yuviler-logicandneuralnetworksdagstuhlseminar25061-2025","author_short":["Belle, V.","Benedikt, M.","Drachsler-Cohen, D.","Neider, D.","Yuviler, T."],"bibdata":{"bibtype":"techreport","type":"techreport","title":"Logic and Neural Networks (Dagstuhl Seminar 25061)","copyright":"Creative Commons Attribution 4.0 International license, info:eu-repo/semantics/openAccess","issn":"2192-5283","url":"https://drops.dagstuhl.de/entities/document/10.4230/DagRep.15.2.1","doi":"10.4230/DAGREP.15.2.1","abstract":"Logic and learning are central to Computer Science, and in particular to AI-related research. Already Alan Turing envisioned in his 1950 “Computing Machinery and Intelligence” paper a combination of statistical (ab initio) machine learning and an “unemotional” symbolic language such as logic. The combination of logic and learning has received new impetus from the spectacular success of deep learning systems.","language":"en","urldate":"2025-10-10","institution":"Schloss Dagstuhl – Leibniz-Zentrum für Informatik","author":[{"propositions":[],"lastnames":["Belle"],"firstnames":["Vaishak"],"suffixes":[]},{"propositions":[],"lastnames":["Benedikt"],"firstnames":["Michael"],"suffixes":[]},{"propositions":[],"lastnames":["Drachsler-Cohen"],"firstnames":["Dana"],"suffixes":[]},{"propositions":[],"lastnames":["Neider"],"firstnames":["Daniel"],"suffixes":[]},{"propositions":[],"lastnames":["Yuviler"],"firstnames":["Tom"],"suffixes":[]}],"year":"2025","note":"Artwork Size: 20 pages, 2350077 bytes Issue: 2 Medium: application/pdf Publication Title: Dagstuhl Reports (DagRep) Volume: 15","keywords":"Computing methodologies → Artificial intelligence, Computing methodologies → Logical and relational learning, Computing methodologies → Machine learning approaches, General and reference → Surveys and overviews, Theory of computation → Constraint and logic programming, Theory of computation → Modal and temporal logics, Theory of computation → Models of learning, computational complexity, databases, learning theory, logic, machine learning, safety, verification","pages":"1–20","bibtex":"@techreport{belle_logic_2025,\n\ttitle = {Logic and {Neural} {Networks} ({Dagstuhl} {Seminar} 25061)},\n\tcopyright = {Creative Commons Attribution 4.0 International license, info:eu-repo/semantics/openAccess},\n\tissn = {2192-5283},\n\turl = {https://drops.dagstuhl.de/entities/document/10.4230/DagRep.15.2.1},\n\tdoi = {10.4230/DAGREP.15.2.1},\n\tabstract = {Logic and learning are central to Computer Science, and in particular to AI-related research. Already Alan Turing envisioned in his 1950 “Computing Machinery and Intelligence” paper a combination of statistical (ab initio) machine learning and an “unemotional” symbolic language such as logic. The combination of logic and learning has received new impetus from the spectacular success of deep learning systems.},\n\tlanguage = {en},\n\turldate = {2025-10-10},\n\tinstitution = {Schloss Dagstuhl – Leibniz-Zentrum für Informatik},\n\tauthor = {Belle, Vaishak and Benedikt, Michael and Drachsler-Cohen, Dana and Neider, Daniel and Yuviler, Tom},\n\tyear = {2025},\n\tnote = {Artwork Size: 20 pages, 2350077 bytes\nIssue: 2\nMedium: application/pdf\nPublication Title: Dagstuhl Reports (DagRep)\nVolume: 15},\n\tkeywords = {Computing methodologies → Artificial intelligence, Computing methodologies → Logical and relational learning, Computing methodologies → Machine learning approaches, General and reference → Surveys and overviews, Theory of computation → Constraint and logic programming, Theory of computation → Modal and temporal logics, Theory of computation → Models of learning, computational complexity, databases, learning theory, logic, machine learning, safety, verification},\n\tpages = {1--20},\n}\n\n\n\n\n\n\n\n\n\n\n\n","author_short":["Belle, V.","Benedikt, M.","Drachsler-Cohen, D.","Neider, D.","Yuviler, T."],"key":"belle_logic_2025","id":"belle_logic_2025","bibbaseid":"belle-benedikt-drachslercohen-neider-yuviler-logicandneuralnetworksdagstuhlseminar25061-2025","role":"author","urls":{"Paper":"https://drops.dagstuhl.de/entities/document/10.4230/DagRep.15.2.1"},"keyword":["Computing methodologies → Artificial intelligence","Computing methodologies → Logical and relational learning","Computing methodologies → Machine learning approaches","General and reference → Surveys and overviews","Theory of computation → Constraint and logic programming","Theory of computation → Modal and temporal logics","Theory of computation → Models of learning","computational complexity","databases","learning theory","logic","machine learning","safety","verification"],"metadata":{"authorlinks":{}},"downloads":0,"html":""},"bibtype":"techreport","biburl":"https://bibbase.org/zotero/alukina","dataSources":["Cfgnp5s4HQSBd8tAf"],"keywords":["computing methodologies → artificial intelligence","computing methodologies → logical and relational learning","computing methodologies → machine learning approaches","general and reference → surveys and overviews","theory of computation → constraint and logic programming","theory of computation → modal and temporal logics","theory of computation → models of learning","computational complexity","databases","learning theory","logic","machine learning","safety","verification"],"search_terms":["logic","neural","networks","dagstuhl","seminar","25061","belle","benedikt","drachsler-cohen","neider","yuviler"],"title":"Logic and Neural Networks (Dagstuhl Seminar 25061)","year":2025}