Hypercube-Based Methods for Symbolic Knowledge Extraction: Towards a Unified Model

by Federico Sabbatini, Giovanni Ciatto, Roberta Calegari, and Andrea Omicini


Symbolic knowledge-extraction (SKE) algorithms proposed by the XAI community to obtain human-intelligible explanations for opaque machine learning predictors are currently being studied and developed with growing interest, also in order to achieve believability in interactions. However, choosing the most adequate extraction procedure amongst the many existing in the literature is becoming more and more challenging, as the amount of available methods increases. In fact, most of the proposed algorithms come with constraints over their applicability. In this paper we focus upon a quite general class of SKE techniques, namely hypercube-based methods. Despite being commonly considered regression-specific, we discuss why hypercube-based SKE methods are flexible enough to deal with classification problems as well. More generally, we propose a common generalised model for hypercube-based methods, and we show how they can be exploited to perform SKE on datasets, predictors, or learning tasks of any sort.

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    author = {Sabbatini, Federico and Ciatto, Giovanni and Calegari, Roberta and Omicini, Andrea},
    booktitle = {WOA 2022 -- 23rd Workshop ``From Objects to Agents''},
    dblp = {conf/woa/SabbatiniCCO22},
    editor = {Ferrando, Angelo and Mascardi, Viviana},
    iris = {11585/899358},
    issn = {1613-0073},
    keywords = {Explainable AI; Knowledge extraction; Interpretable prediction; PSyKE},
    month = nov,
    numpages = 13,
    pages = {48--60},
    publisher = {Sun SITE Central Europe, RWTH Aachen University},
    scholar = {8614662013642803891},
    series = {CEUR Workshop Proceedings},
    subseries = {AIxIA Series},
    title = {Hypercube-Based Methods for Symbolic Knowledge Extraction: Towards a Unified Model},
    url = {http://ceur-ws.org/Vol-3261/paper4.pdf},
    urlopenaccess = {http://ceur-ws.org/Vol-3261/paper4.pdf},
    urlpdf = {http://ceur-ws.org/Vol-3261/paper4.pdf},
    volume = 3261,
    year = 2022