Relational Action Bases: Formalization, Effective Safety Verification, and Invariants (Extended Version)
This work addresses verification challenges in AI, Business Process Management, and Database Theory, but is incremental as it builds on existing models and verification techniques.
The authors tackled the problem of verifying dynamic systems with unbounded relational states and complex actions by introducing relational action bases (RABs), and demonstrated effectiveness on a benchmark of data-aware business processes with an SMT-based backward search approach.
Modeling and verification of dynamic systems operating over a relational representation of states are increasingly investigated problems in AI, Business Process Management, and Database Theory. To make these systems amenable to verification, the amount of information stored in each relational state needs to be bounded, or restrictions are imposed on the preconditions and effects of actions. We introduce the general framework of relational action bases (RABs), which generalizes existing models by lifting both these restrictions: unbounded relational states can be evolved through actions that can quantify both existentially and universally over the data, and that can exploit numerical datatypes with arithmetic predicates. We then study parameterized safety of RABs via (approximated) SMT-based backward search, singling out essential meta-properties of the resulting procedure, and showing how it can be realized by an off-the-shelf combination of existing verification modules of the state-of-the-art MCMT model checker. We demonstrate the effectiveness of this approach on a benchmark of data-aware business processes. Finally, we show how universal invariants can be exploited to make this procedure fully correct.