Integrating Model Checking and Inductive Logic Programming

Dalal Alrajeh, Alessandra Russo, Sebastián Uchitel, Jeff Kramer

Abstract: Inductive Logic Programming can be used to provide automated support to help correct the errors identified by model checking, which in turn provides the relevant context for learning hypotheses that are meaningful within the domain of interest. Model checking and Inductive Logic Programming can thus be seen as two complementary approaches with much to gain from their integration. In this paper we present a general framework for such an integration, discuss its main characteristics and present an overview of its application.