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Neural-Symbolic Learning and Reasoning: Contributions and Challenges
City University, London, England.
Universität Osnabrück, Osnabrück, Germany.
Department of Computer Science, KU Leuven, Leuven, Belgium.ORCID iD: 0000-0002-6860-6303
Univ. of St. Andrews, London, England.
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2015 (English)In: Knowledge Representation and Reasoning: Integrating Symbolic and Neural Approaches - Papers from the 2015 AAAI Spring Symposium, Technical Report, AAAI Press, 2015, Vol. SS-15-03, p. 18-21Conference paper, Published paper (Refereed)
Abstract [en]

Neural-symbolic computation aims at integrating robust connectionist learning algorithms with sound symbolic rea-soning. The recent impact of neural learning, in particular of deep networks, has led to the creation of new representa-tions that have, so far, not really been used for reasoning. Results on neural-symbolic computation have shown to of-fer powerful alternatives for knowledge representation, learning and inference in neural computation. This paper presents key challenges and contributions of neural-symbolic computation to this area.

Place, publisher, year, edition, pages
AAAI Press, 2015. Vol. SS-15-03, p. 18-21
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Computer and Information Sciences
Identifiers
URN: urn:nbn:se:oru:diva-91874DOI: 10.13140/2.1.1779.4243ISBN: 9781577357070 (print)OAI: oai:DiVA.org:oru-91874DiVA, id: diva2:1556181
Conference
AAAI Spring Symposium - Knowledge Representation and Reasoning: Integrating Symbolic and Neural Approaches, Stanford University, Palo Alto, CA, USA, March 23-25, 2015
Available from: 2021-05-20 Created: 2021-05-20 Last updated: 2021-05-24Bibliographically approved

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De Raedt, Luc

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