Heuristic Minimization Modulo Theory of Modal Decision Trees Class-Formulas

One way towards the design of trustable, explainable, and interpretable artificial intelligence models is to focus on symbolic machine learning models, such as decision trees. While decision trees are already intelligible in principle, the logical rules they enclose may still be redundant, in particular with respect to some underlying theory. Moreover, propositional decision trees have been recently generalized to the case of modal logic; modal decision trees turn out to be more expressive than propositional ones, so their corresponding modal rules are proportionally harder to understand and minimize. In this paper we approach the problem of minimizing logical rules extracted from (modal) decision trees modulo some external theory.