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Neural-Symbolic Learning and Reasoning: Contributions and Challenges
BibTeX
@MISC{Garcez_neural-symboliclearning,
author = {Artur D’avila Garcez and Tarek R. Besold and Luc De Raedt and Peter Földiak and Pascal Hitzler and Thomas Icard and Kai-uwe Kühnberger and Luis C. Lamb and Risto Miikkulainen and Daniel L. Silver},
title = {Neural-Symbolic Learning and Reasoning: Contributions and Challenges},
year = {}
}
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Abstract
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.
