Logic Programming library for Machine Learning: API design and prototype

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.

parole chiave   logic programming, machine learning, API, 2P-Kt

presentazione di riferimento

 Logic Programming library for Machine Learning: API design and prototype (CILC 2022, 01/08/2022) — Giovanni Ciatto (Giovanni Ciatto, Matteo Castiglio, Roberta Calegari)

evento origine

 CILC 2022

rivista o collana

 CEUR Workshop Proceedings (CEUR-WS.org)

progetto finanziatore

 EXPECTATION — Personalized Explainable Artificial Intelligence for decentralized agents with heterogeneous knowledge (01/04/2021–31/03/2024)

 StairwAI — Stairway to AI: Ease the Engagement of Low-Tech users to the AI-on-Demand platform through AI (01/01/2021–31/12/2023)

pubblicazione di riferimento per presentazione

 Logic Programming library for Machine Learning: API design and prototype (CILC 2022, 01/08/2022) — Giovanni Ciatto (Giovanni Ciatto, Matteo Castiglio, Roberta Calegari)