Learning Disjunctive Logic Programs from Nondeterministic Interpretation Transitions

Abstract

Inductive logic programming (ILP) is a framework of learning logic programs from examples and background knowledge. In some real-world applications, we are particularly interested in learning aspects of system dynamics that are characterized by state transitions for which logic programs are shown to be expressive. In this work, we study this particular form of ILP—learning logic programs from state/interpretation transitions. Firstly, we define a state transition operator for disjunctive logic programs which generalizes the immediate consequence operator for normal logic programs. Secondly, we formulate two resolutions to simplify logic programs under state transition and study their properties. Finally, we put forward an inductive learning framework, which is shown to provide a sound and complete procedure for learning disjunctive logic programs from state transitions. A prototype system is implemented in Python and evaluated with randomly generated examples of well-known Boolean network benchmarks.