Bisimulation Learning

TL;DR

This paper presents a data-driven method for learning finite bisimulations in large state transition systems, enabling faster verification and interpretable system abstractions.

Contribution

It introduces a novel counterexample-guided inductive synthesis approach for stutter-insensitive bisimulation learning using satisfiability modulo theory solving.

Findings

Faster verification compared to existing tools on benchmarks.

Produces succinct, interpretable system abstractions.

Effective for verifying linear temporal logic without next operator.

Abstract

We introduce a data-driven approach to computing finite bisimulations for state transition systems with very large, possibly infinite state space. Our novel technique computes stutter-insensitive bisimulations of deterministic systems, which we characterize as the problem of learning a state classifier together with a ranking function for each class. Our procedure learns a candidate state classifier and candidate ranking functions from a finite dataset of sample states; then, it checks whether these generalise to the entire state space using satisfiability modulo theory solving. Upon the affirmative answer, the procedure concludes that the classifier constitutes a valid stutter-insensitive bisimulation of the system. Upon a negative answer, the solver produces a counterexample state for which the classifier violates the claim, adds it to the dataset, and repeats learning and checking in a counterexample-guided inductive synthesis loop until a valid bisimulation is found. We demonstrate on a range of benchmarks from reactive verification and software model checking that our method yields faster verification results than alternative state-of-the-art tools in practice. Our method produces succinct abstractions that enable an effective verification of linear temporal logic without next operator, and are interpretable for system diagnostics.