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.

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    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