Neural-Symbolic Learning and Reasoning: Contributions and Challenges
Artur d’Avila Garcez, Tarek R. Besold, Luc De Raedt, Péter Földiák, Pascal Hitzler, Thomas Icard, Kai‐Uwe Kühnberger, Luís C. Lamb, Risto P Miikkulainen, Daniel L. Silver · City Research Online (City University London) · 2015
The goal of neural-symbolic computation is to integrate ro-bust connectionist learning and sound symbolic reasoning. With the recent advances in connectionist learning, in par-ticular deep neural networks, forms of representation learn-ing 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. 1.