Quantitative Supermartingale Certificates
Alessandro Abate, Mirco Giacobbe, Diptarko Roy
摘要
Abstract We introduce a general methodology for quantitative model checking and control synthesis with supermartingale certificates. We show that every specification that is invariant to time shifts admits a stochastic invariant that bounds its probability from below; for systems with general state space, the stochastic invariant bounds this probability as closely as desired; for systems with finite state space, it quantifies it exactly. Our result enables the extension of every certificate for the almost-sure satisfaction of shift-invariant specifications to its quantitative counterpart, ensuring completeness up to an approximation in the general case and exactness in the finite-state case. This generalises and unifies existing supermartingale certificates for quantitative verification and control under reachability, safety, reach-avoidance, and stability specifications, as well as asymptotic bounds on accrued costs and rewards. Furthermore, our result provides the first supermartingale certificate for computing upper and lower bounds on the probability of satisfying ω -regular and linear temporal logic specifications. We present an algorithm for quantitative ω -regular verification and control synthesis based on our method and demonstrate its practical efficacy on several infinite-state examples.
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了最后一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
引用它的顶会 Paper6
- Let a Neural Network be Your InvariantMirco Giacobbe, Daniel Kroening, Abhinandan Pal, Michael TautschnigNeurIPS 2025 · 被引用 6 次
- A Hierarchy of Supermartingales for ω-Regular VerificationSatoshi Kura, Hiroshi UnnoPLDI 2026 · 被引用 1 次
- Complete ω-Regular Supermartingale CertificatesAlessandro Abate, Mirco Giacobbe, Sergey Ichtchenko, Diptarko RoyLICS 2026
- Supermartingales for Unique Fixed Points: A Unified Approach to Lower Bound VerificationSatoshi Kura, Hiroshi Unno, Takeshi TsukadaPLDI 2026
- Liveness Proofs for Hardware Model CheckingNils Froleyks, Emily Yu, Bart Bogaerts, Armin Biere 等CAV 2026
它引用的顶会 Paper26
- Learning Control Policies for Stochastic Systems with Reach-Avoid GuaranteesDorde Zikelic, Mathias Lechner, Thomas A. Henzinger, Krishnendu ChatterjeeAAAI 2023 · 被引用 50 次
- Aiming low is harder: induction for lower bounds in probabilistic program verificationMarcel Hark, Benjamin Lucien Kaminski, Jürgen Giesl, Joost-Pieter KatoenPOPL 2020 · 被引用 47 次
- Stability Verification in Stochastic Control Systems via Neural Network SupermartingalesMathias Lechner, Dorde Zikelic, Krishnendu Chatterjee, Thomas A. HenzingerAAAI 2022 · 被引用 45 次
- Compositional Policy Learning in Stochastic Control Systems with Formal GuaranteesDorde Zikelic, Mathias Lechner, Abhinav Verma, Krishnendu Chatterjee 等NeurIPS 2023 · 被引用 31 次
- Sound and Complete Certificates for Quantitative Termination Analysis of Probabilistic ProgramsKrishnendu Chatterjee, Amir Kafshdar Goharshady, Tobias Meggendorfer, Dorde ZikelicCAV 2022 · 被引用 30 次
相关 Paper
- Stochastic Omega-Regular Verification and Control with SupermartingalesAlessandro Abate, Mirco Giacobbe, Diptarko RoyCAV 2024 · 被引用 13 次
- Supermartingale Certificates for Quantitative Omega-Regular Verification and ControlThomas A. Henzinger, Kaushik Mallik, Pouya Sadeghi, Dorde ZikelicCAV 2025 · 被引用 6 次
- Policy Verification in Stochastic Dynamical Systems Using Logarithmic Neural CertificatesThom Badings, Wietze Koops, Sebastian Junges, Nils JansenCAV 2025 · 被引用 1 次
- Robustness Verification of Deep Reinforcement Learning Based Control Systems Using Reward MartingalesDapeng Zhi, Peixin Wang, Cheng Chen, Min ZhangAAAI 2024 · 被引用 4 次
- Deductive Synthesis of Reinforcement Learning Agents for Infinite Horizon TasksYuning Wang, He ZhuCAV 2025
