Adapting an AI Planning Heuristic for Directed Model Checking

There is a growing body of work on directed model checking, which improves the falsi cation of safety properties by providing heuristic functions that can guide the search quickly towards short error paths. Techniques of this kind have also been made very successful in the area of AI Planning. Our main technical contribution is the adaptation of the most successful heuristic function from AI Planning to the model checking context, yielding a new heuristic for directed model checking. The heuristic is based on solving an abstracted problem in every search state. We adapt the abstraction and its solution to networks of communicating automata annotated with (constraints and e ects on) integer variables. Since our ultimate goal in this research is to also take into account clock variables, as used in timed automata, our techniques are implemented inside UPPAAL. We run experiments in some toy benchmarks for timed automata, and in two timed automata case studies originating from an industrial project. Compared to both blind search and some previously proposed heuristic functions, we consistently obtain dramatic search space reductions, resulting in dramatic reductions of runtime and memory requirements.