AAAI Publications, 2015 AAAI Spring Symposium Series

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Neural-Symbolic Learning and Reasoning: Contributions and Challenges
Artur d'Avila Garcez, Tarek R. Besold, Luc de Raedt, Peter Földiak, Pascal Hitzler, Thomas Icard, Kai-Uwe Kühnberger, Luis C. Lamb, Risto Miikkulainen, Daniel L. Silver

Last modified: 2015-03-11


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.


Neural-Symbolic Computation; Knowledge representation; Relational learning; Symbolic learning; Transfer learning

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