mlsanityData to 7 Oct 2026

Automated Verification and Synthesis of Stochastic Hybrid Systems: A Survey

arXiv 2101.07491 ↗First posted 19 Jan 2021cs.LO cs.SY eess.SYcited by 20

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Abstract (arXiv)

Stochastic hybrid systems have received significant attentions as a relevant modelling framework describing many systems, from engineering to the life sciences: they enable the study of numerous applications, including transportation networks, biological systems and chemical reaction networks, smart energy and power grids, and beyond. Automated verification and policy synthesis for stochastic hybrid systems can be inherently challenging: this is due to the heterogeneity of their dynamics (presence of continuous and discrete components), the presence of uncertainty, and in some applications the large dimension of state and input sets. Over the past few years, a few hundred articles have investigated these models, and developed diverse and powerful approaches to mitigate difficulties encountered in the analysis and synthesis of such complex stochastic systems. In this survey, we overview the most recent results in the literature and discuss different approaches, including (in)finite abstractions, verification and synthesis for temporal logic specifications, stochastic similarity relations, (control) barrier certificates, compositional techniques, and a selection of results on continuous-time stochastic systems; we finally survey recently developed software tools that implement the discussed approaches. Throughout the manuscript we discuss a few open topics to be considered as potential future research directions: we hope that this survey will guide younger researchers through a comprehensive understanding of the various challenges, tools, and solutions in this enticing and rich scientific area.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Jan 2021.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 3; 2023: 4; 2024: 2; 2025: 5; 2026: 4.

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  1. 2024Robust Markov Decision Processes: A Place Where AI and Formal Methods Meetcited by 18
  2. 2023Robust Control for Dynamical Systems With Non-Gaussian Noise via Formal Abstractionscited by 16
  3. 2021Sampling-Based Robust Control of Autonomous Systems with Non-Gaussian Noisecited by 13
  4. 2022Probabilities Are Not Enough: Formal Controller Synthesis for Stochastic Dynamical Models with Epistemic Uncertaintycited by 11
  5. 2024Data-Driven Distributionally Robust Safety Verification Using Barrier Certificates and Conditional Mean Embeddingscited by 9
  6. 2023Decision-Making Under Uncertainty: Beyond Probabilitiescited by 8
  7. 2021Correct-by-construction reach-avoid control of partially observable linear stochastic systemscited by 5
  8. 2022Interval Markov Decision Processes with Continuous Action-Spacescited by 2
  9. 2023A Survey on Event Prediction Methods from a Systems Perspective: Bringing Together Disparate Research Areascited by 2
  10. 2022Formal Controller Synthesis for Markov Jump Linear Systems with Uncertain Dynamicscited by 1
  11. 2025Formal Control for Uncertain Systems via Contract-Based Probabilistic Surrogates (Extended Version)cited by 1
  12. 2023Correct-by-Construction Control for Stochastic and Uncertain Dynamical Models via Formal Abstractionscited by 0
8 more
  1. 2025Average Reward Reinforcement Learning for Omega-Regular and Mean-Payoff Objectivescited by 0
  2. 2025Control Synthesis in Partially Observable Environments for Complex Perception-Related Objectivescited by 0
  3. 2025Comparing Labeled Markov Chains: A Cantor-Kantorovich Approachcited by 0
  4. 2025Neural Proofs for Sound Verification and Control of Complex Systemscited by 0
  5. 2026Kernel-Based Learning of Safety Barrierscited by 0
  6. 2026Interval POMDP Shielding for Imperfect-Perception Agentscited by 0
  7. 2026Safety Certification is Classificationcited by 0
  8. 2026Sound Probabilistic Safety Bounds for Large Language Modelscited by 0