Logic Programming library for Machine Learning: API design and prototype
by Giovanni Ciatto, Matteo Castigliò, and Roberta Calegari
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.
@inproceedings{logicapiml-cilc2022,
author = {Ciatto, Giovanni and Castigliò, Matteo and Calegari, Roberta},
booktitle = {CILC 2022 -- Italian Conference on Computational Logic},
editor = {Calegari, Roberta and Ciatto, Giovanni and Omicini, Andrea},
issn = {1613-0073},
keywords = {logic programming, machine learning, API, 2P-Kt},
location = {Bologna, Italy},
numpages = 15,
pages = {104--118},
publisher = {CEUR-WS},
series = {ceurws},
subseries = {AI*IA Series},
title = {Logic Programming library for Machine Learning: {API} design and prototype},
url = {http://ceur-ws.org/Vol-3204/paper_12.pdf},
urlopenaccess = {http://ceur-ws.org/Vol-3204/paper_12.pdf},
urlpdf = {http://ceur-ws.org/Vol-3204/paper_12.pdf},
volume = 3204,
year = 2022
}