Using Predicate Abstraction to Generate Heuristic Functions in UPPAAL

We focus on checking safety properties in networks of extended timed automata, with the well-known UPPAAL system. We show how to use predicate abstraction, in the sense used in model checking, to generate search guidance, in the sense used in Artificial Intelligence (AI). This contributes another family of heuristic functions to the growing body of work on directed model checking. The overall methodology follows the pattern databaseapproach from AI: the abstract state space is exhaustively built in a pre-process, and used as a lookup table during search. While typically pattern databases use rather primitive abstractions ignoring some of the relevant symbols, we use predicate abstraction, dividing the state space into equivalence classes with respect to a list of logical expressions (predicates). We empirically explore the behavior of the resulting family of heuristics, in a meaningful set of benchmarks. In particular, while several challenges remain open, we show that one can easily obtain heuristic functions that are competitive with the state-of-the-art in directed model checking.