  We present a fast and scalable algorithm to induce non-monotonic logic
programs from statistical learning models. We reduce the problem of search for
best clauses to instances of the High-Utility Itemset Mining (HUIM) problem. In
the HUIM problem, feature values and their importance are treated as
transactions and utilities respectively. We make use of TreeExplainer, a fast
and scalable implementation of the Explainable AI tool SHAP, to extract locally
important features and their weights from ensemble tree models. Our experiments
with UCI standard benchmarks suggest a significant improvement in terms of
classification evaluation metrics and running time of the training algorithm
compared to ALEPH, a state-of-the-art Inductive Logic Programming (ILP) system.
