Fast Symbolic Algorithms for Omega-Regular Games under Strong Transition Fairness

Abstract

We consider fixpoint algorithms for two-player games on graphs with 𝜔-regular winning conditions, where the environment is constrained by a strong transition fairness assumption. Strong transition fairness is a widely occurring special case of strong fairness, which requires that any execution is strongly fair with respect to a specified set of live edges: whenever the source vertex of a live edge is visited infinitely often along a play, the edge itself is traversed infinitely often along the play as well.We show that, surprisingly, strong transition fairness retains the algorithmic characteristics of the fixpoint algorithms for 𝜔-regular games-the new algorithms can be obtained simply by replacing certain occurrences of the controllable predecessor by a new almost sure predecessor operator. For Rabin games with 𝑘 pairs, the complexity of the new algorithm is 𝑂 (𝑛 𝑘+2 𝑘!) symbolic steps, which is independent of the number of live edges in the strong transition fairness assumption. Further, we show that GR(1) specifications with strong transition fairness assumptions can be solved with a 3-nested fixpoint algorithm, same as the usual algorithm. In contrast, strong fairness necessarily requires increasing the alternation depth depending on the number of fairness assumptions.We get symbolic algorithms for (generalized) Rabin, parity and GR(1) objectives under strong transition fairness assumptions as well as a direct symbolic algorithm for qualitative winning in stochastic 𝜔-regular games that runs in 𝑂 (𝑛 𝑘+2 𝑘!) symbolic steps, improving the state of the art. Previous approaches for handling fairness assumptions would either increase the alternation depth of the fixpoint algorithm or require an up-front automata-theoretic construction that would increase the state space, or both.Finally, we have implemented a BDD-based synthesis engine based on our algorithm. We show on a set of synthetic and real benchmarks that our algorithm is scalable, parallelizable, and outperforms previous algorithms by orders of magnitude.All proofs can be found in the appendix.

ARCH-COMP22 Category Report: Stochastic Models

“…The tool Mascot-SDS [62,61,60,5,6] is currently only handling synthesis of formally verified controllers for almost sure satisfaction (i.e. satisfaction with probability 1) of infinite-horizon specifications.…”