Logic Programming library for Machine Learning: API design and prototype

@talk{logicapi4mlcilc2022,
    abstract = {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.},
    address = {Facolt\`{a} di Ingegneria (Bologna)},
    apice = {LogicApi4MlCilc2022},
    author = {Ciatto, Giovanni and Castiglio, Matteo and Calegari, Roberta},
    date = {2022-08-01},
    language = {en},
    month = aug,
    sort = {talk},
    speaker = {Ciatto, Giovanni},
    title = {Logic Programming library for Machine Learning: API design and prototype},
    type = {Talk},
    url = {https://github.com/pikalab-unibo/cilc-2022-logic-api-ml-talk},
    year = 2022
}