Garcez, A., Besold, T. R., Raedt, L., Foldiak, P., Hitzler, P., Icard, T., Kuhnberger, K-U., Lamb, L. C., Miikkulainen, R. & Silver, D. L. (2015). Neural-Symbolic Learning and Reasoning: Contributions and Challenges. Paper presented at the 2015 AAAI Spring Symposium Series, 23-03-2015 - 25-03-2015, Stanford University, USA.
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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.
|Item Type:||Conference or Workshop Item (Paper)|
|Subjects:||Q Science > QA Mathematics > QA75 Electronic computers. Computer science
R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
|Divisions:||School of Informatics > Department of Computing|
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