Stochastic Omega-Regular Verification and Control with Supermartingales
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Abstract (arXiv)
We present for the first time a supermartingale certificate for ω-regular specifications. We leverage the Robbins & Siegmund convergence theorem to characterize supermartingale certificates for the almost-sure acceptance of Streett conditions on general stochastic processes, which we call Streett supermartingales. This enables effective verification and control of discrete-time stochastic dynamical models with infinite state space under ω-regular and linear temporal logic specifications. Our result generalises reachability, safety, reach-avoid, persistence and recurrence specifications; our contribution applies to discrete-time stochastic dynamical models and probabilistic programs with discrete and continuous state spaces and distributions, and carries over to deterministic models and programs. We provide a synthesis algorithm for control policies and Streett supermartingales as proof certificates for ω-regular objectives, which is sound and complete for supermartingales and control policies with polynomial templates and any stochastic dynamical model whose post-expectation is expressible as a polynomial. We additionally provide an optimisation of our algorithm that reduces the problem to satisfiability modulo theories, under the assumption that templates and post-expectation are in piecewise linear form. We have built a prototype and have demonstrated the efficacy of our approach on several exemplar ω-regular verification and control synthesis problems.
Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). First posted on arXiv 27 May 2024.
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Citing papers per year: 2024: 2; 2025: 4; 2026: 5.
11 papers.
- 2024Neural Model Checkingcited by 6
- 2025Pick-to-Learn for Systems and Control: Data-driven Synthesis with State-of-the-art Safety Guaranteescited by 5
- 2024Policy Verification in Stochastic Dynamical Systems Using Logarithmic Neural Certificatescited by 4
- 2025Proof-Carrying Neuro-Symbolic Codecited by 1
- 2025VeRecycle: Reclaiming Guarantees from Probabilistic Certificates for Stochastic Dynamical Systems after Changecited by 0
- 2025Neural Proofs for Sound Verification and Control of Complex Systemscited by 0
- 2026Kernel-Based Learning of Safety Barrierscited by 0
- 2026Value Functions as Supermartingale Certificatescited by 0
- 2026Entropy Objectives in Markov Decision Processescited by 0
- 2026Supermartingale Certificates for Parametric MDPscited by 0
- 2026Runtime Monitoring of Supermartingale Certificatescited by 0
- 2026Runtime Monitoring of Supermartingale Certificates14 Oct 2026
- 2026Supermartingale Certificates for Parametric MDPs11 Sep 2026
- 2026Entropy Objectives in Markov Decision Processes19 Jun 2026
- 2026Value Functions as Supermartingale Certificates29 May 2026
- 2026Kernel-Based Learning of Safety Barriers17 Jan 2026
- 2025Neural Proofs for Sound Verification and Control of Complex Systems20 Dec 2025
- 2025Pick-to-Learn for Systems and Control: Data-driven Synthesis with State-of-the-art Safety Guarantees4 Dec 2025
- 2025VeRecycle: Reclaiming Guarantees from Probabilistic Certificates for Stochastic Dynamical Systems after Change20 May 2025
- 2025Proof-Carrying Neuro-Symbolic Code16 Apr 2025
- 2024Neural Model Checking31 Oct 2024
- 2024Policy Verification in Stochastic Dynamical Systems Using Logarithmic Neural Certificates2 Jun 2024