Book
Conclusions.
| Title: | Conclusions. |
|---|---|
| Authors: | d΄Avila Garcez, Artur S., Lamb, Luís C., Gabbay, Dov M. |
| Source: | Neural-symbolic Cognitive Reasoning; 2009, p169-180, 12p |
| Abstract: | This chapter reviews the neural-symbolic approach presented in this book and provides a summary of the overall neural-symbolic cognitive model. The book deals with how to represent, learn, and compute expressive forms of symbolic knowledge using neural networks. We believe this is the way forward towards the provision of an integrated system of expressive reasoning and robust learning. The provision of such a system, integrating the two most fundamental phenomena of intelligent cognitive behaviour, has been identified as a key challenge for computer science [255]. Our goal is to produce computational models with integrated reasoning and learning capability, in which neural networks provide the machinery necessary for cognitive computation and learning, while logic provides practical reasoning and explanation capabilities to the neural models, facilitating the necessary interaction with the outside world. [ABSTRACT FROM AUTHOR] |
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| DOI: | 10.1007/978-3-540-73246-4_13 |
| Database: | Complementary Index |
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