In this paper we address the problem of hybridising symbolic and sub-symbolic approaches in artificial intelligence, following the purpose of creating flexible and data-driven systems, which are simultaneously comprehensible and capable of automated learning. In particular, we propose a logic API for supervised machine learning, enabling logic programmers to exploit neural networks - among the others - in their programs. Accordingly, we discuss the design and architecture of a library reifying APIs for the Prolog language in the 2P-Kt logic ecosystem. Finally, we discuss a number of snippets aimed at exemplifying the major benefits of our approach when designing hybrid systems.

Logic Programming library for Machine Learning: API design and prototype

Ciatto G.;Calegari R.
2022

Abstract

In this paper we address the problem of hybridising symbolic and sub-symbolic approaches in artificial intelligence, following the purpose of creating flexible and data-driven systems, which are simultaneously comprehensible and capable of automated learning. In particular, we propose a logic API for supervised machine learning, enabling logic programmers to exploit neural networks - among the others - in their programs. Accordingly, we discuss the design and architecture of a library reifying APIs for the Prolog language in the 2P-Kt logic ecosystem. Finally, we discuss a number of snippets aimed at exemplifying the major benefits of our approach when designing hybrid systems.
CEUR Workshop Proceedings
104
118
Ciatto G.; Castiglio M.; Calegari R.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/903799
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