Logic Programming library for Machine Learning: API design and prototype

In this paper we address the problem of hybridising logic and sub-symbolic approaches to artificial intelligence, following the purpose of creating flexible and data-driven systems, which are simultaneously comprehensible and capable of automated learning. In particular, in this paper 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 our API for the Prolog language, on top of the 2P-Kt logic ecosystem. Finally, we discuss a number of snippets aimed at exemplifying the major benefits of our approach when it comes to design hybrid systems.