  We focus on the problem of inducing logic programs that explain models
learned by the support vector machine (SVM) algorithm. The top-down sequential
covering inductive logic programming (ILP) algorithms (e.g., FOIL) apply
hill-climbing search using heuristics from information theory. A major issue
with this class of algorithms is getting stuck in a local optimum. In our new
approach, however, the data-dependent hill-climbing search is replaced with a
model-dependent search where a globally optimal SVM model is trained first,
then the algorithm looks into support vectors as the most influential data
points in the model, and induces a clause that would cover the support vector
and points that are most similar to that support vector. Instead of defining a
fixed hypothesis search space, our algorithm makes use of SHAP, an
example-specific interpreter in explainable AI, to determine a relevant set of
features. This approach yields an algorithm that captures SVM model's
underlying logic and outperforms %GG: the FOIL algorithm --> other ILP
algorithms other ILP algorithms in terms of the number of induced clauses and
classification evaluation metrics. This paper is under consideration for
publication in the journal of "Theory and practice of logic programming".
