Acta Scientific Computer Sciences

Short Communication Volume 4 Issue 2

On the Combination of Deep Learning and Knowledge Representation and Reasoning Techniques

Francesco Cauteruccio*

Department of Mathematics and Computer Science, University of Calabria, Italy

*Corresponding Author: Francesco Cauteruccio, Department of Mathematics and Computer Science, University of Calabria, Italy.

Received: September 25, 2021; Published: January 19, 2022

Abstract

Nowadays, the world of Artificial Intelligence continuously poses researchers to face new challenges. Several contexts have been extensively studied, such as Deep Learning, which helped the research community sheds light on the intricate core of various problems. However, to tackle new challenges with only a single tool does not allow to study them from different points of views. In this short communication, we borrow the context of Knowledge Representation and Reasoning and the techniques therein involved, such as declarative formalism, and we highlight how the combination of Deep Learning with them helps researchers addressing new challenges in several different ways.


Keywords: Deep Learning; Logic Programming; Knowledge Representation and Reasoning; Answer Set Programming; Bioinformatics

References

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  4. Baral Chitta and Michael Gelfond. "Logic programming and knowledge representation”. The Journal of Logic Programming 19 (1994): 73-148.
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  6. Erdem Esra Michael Gelfond and Nicola Leone. "Applications of answer set programming”. AI Magazine3 (2016): 53-68.
  7. Calimeri Francesco., et al. “A logic-based framework leveraging neural networks for studying the evolution of neurological disorders”. Theory and Practice of Logic Programming1 (2021): 80-124.

Citation

Citation: Francesco Cauteruccio. “On the Combination of Deep Learning and Knowledge Representation and Reasoning Techniques". Acta Scientific Computer Sciences 4.2 (2022): 49-50.

Copyright

Copyright: © 2022 Francesco Cauteruccio. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.




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