On the Design of PSyKI: a Platform for Symbolic Knowledge Injection into Sub-Symbolic Predictors

by Matteo Magnini, Giovanni Ciatto, and Andrea Omicini


A long-standing ambition in artificial intelligence is to integrate predictors' inductive features (i.e., learning from examples) with deductive capabilities (i.e., drawing inferences from prior symbolic knowledge). Many algorithms methods in the literature support injection of prior symbolic knowledge into predictors, generally following the purpose of attaining better (i.e., more effective or efficient w.r.t. predictive performance) predictors. However, to the best of our knowledge, running implementations of these algorithms are currently either proof of concepts or unavailable in most cases. Moreover, a unified, coherent software framework supporting them as well as their interchange, comparison and exploitation in arbitrary ML workflows is currently missing. Accordingly, in this paper we present PSyKI, a platform providing general-purpose support to symbolic knowledge injection into predictors via different algorithms.

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    author = {Magnini, Matteo and Ciatto, Giovanni and Omicini, Andrea},
    booktitle = {Explainable and Transparent AI and Multi-Agent Systems},
    chapter = 6,
    dblp = {conf/atal/MagniniCO22},
    doi = {10.1007/978-3-031-15565-9_6},
    editor = {Calvaresi, Davide and Najjar, Amro and Winikoff, Michael and Främling, Kary},
    eisbn = {978-3-031-15565-9},
    eissn = {1611-3349},
    iris = {11585/899511},
    isbn = {978-3-031-15564-2},
    issn = {0302-9743},
    keywords = {Symbolic Knowledge Injection, Explainable AI, XAI, Neural Networks, PSyKI},
    note = {4th International Workshop, EXTRAAMAS 2022, Virtual Event, May 9--10, 2022, Revised Selected Papers},
    pages = {90--108},
    publisher = {Springer},
    scholar = {7587528289517313138},
    scopus = {2-s2.0-85138317005},
    series = {Lecture Notes in Computer Science},
    title = {On the Design of {PSyKI}: a Platform for Symbolic Knowledge Injection into Sub-Symbolic Predictors},
    url = {https://link.springer.com/chapter/10.1007/978-3-031-15565-9_6},
    urlpdf = {https://link.springer.com/content/pdf/10.1007/978-3-031-15565-9_6.pdf},
    volume = 13283,
    wos = {000870042100006},
    year = 2022