Neural-symbolic learning and reasoning: Contributions and challenges
Autor(en): | Garcez, A.D. Besold, T.R. De Raedt, L. Foldiak, P. Hitzler, P. Icard, T. Kiihnberger, K.-U. Lamb, L.C. Miikkulainen, R. Silver, D.L. |
Stichwörter: | Deep neural networks; Knowledge representation, Forms of representation; Neural computations; Neural-symbolic integration; Symbolic computation; Symbolic learning; Symbolic reasoning, Deep learning | Erscheinungsdatum: | 2015 | Herausgeber: | AI Access Foundation | Journal: | AAAI Spring Symposium - Technical Report | Volumen: | SS-15-03 | Startseite: | 18 | Seitenende: | 21 | Zusammenfassung: | The goal of neural-symbolic computation is to integrate robust connectionist learning and sound symbolic reasoning. With the recent advances in connectionist learning, in particular deep neural networks, forms of representation learning have emerged. However, such representations have not become useful for reasoning. Results from neural-symbolic computation have shown to offer powerful alternatives for knowledge representation, learning and reasoning in neural computation. This paper recalls the main contributions and discusses key challenges for neural-symbolic integration which have been identified at a recent Dagstuhl seminar. Copyright © 2015. Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. |
Beschreibung: | Conference of 2015 AAAI Spring Symposium ; Conference Date: 23 March 2015 Through 25 March 2015; Conference Code:113922 |
ISBN: | 9781577357070 | Externe URL: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84987653240&partnerID=40&md5=671cdfc56f1fdf2418b2c0c265e7414f |
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