FAUST2: Formal Abstractions of Uncountable-STate STochastic processes
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Abstract (arXiv)
FAUST2 is a software tool that generates formal abstractions of (possibly non-deterministic) discrete-time Markov processes (dtMP) defined over uncountable (continuous) state spaces. A dtMP model is specified in MATLAB and abstracted as a finite-state Markov chain or Markov decision processes. The abstraction procedure runs in MATLAB and employs parallel computations and fast manipulations based on vector calculus. The abstract model is formally put in relationship with the concrete dtMP via a user-defined maximum threshold on the approximation error introduced by the abstraction procedure. FAUST2 allows exporting the abstract model to well-known probabilistic model checkers, such as PRISM or MRMC. Alternatively, it can handle internally the computation of PCTL properties (e.g. safety or reach-avoid) over the abstract model, and refine the outcomes over the concrete dtMP via a quantified error that depends on the abstraction procedure and the given formula. The toolbox is available at http://sourceforge.net/projects/faust2/
Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Mar 2014.
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Citing papers per year: 2021: 4; 2022: 5; 2023: 1; 2024: 1; 2025: 1.
12 papers.
- 2021Stability Verification in Stochastic Control Systems via Neural Network Supermartingalescited by 24
- 2022Learning Control Policies for Stochastic Systems with Reach-avoid Guaranteescited by 21
- 2021Shielding Atari Games with Bounded Presciencecited by 14
- 2023Compositional Policy Learning in Stochastic Control Systems with Formal Guaranteescited by 12
- 2022LCRL: Certified Policy Synthesis via Logically-Constrained Reinforcement Learningcited by 11
- 2021Scalable Synthesis of Verified Controllers in Deep Reinforcement Learningcited by 5
- 2021Formal Verification of Unknown Dynamical Systems via Gaussian Process Regressioncited by 5
- 2022Learning Provably Stabilizing Neural Controllers for Discrete-Time Stochastic Systemscited by 4
- 2022SOCKS: A Stochastic Optimal Control and Reachability Toolbox Using Kernel Methodscited by 1
- 2022Learning Stabilizing Policies in Stochastic Control Systemscited by 1
- 2024Certified Policy Verification and Synthesis for MDPs under Distributional Reach-avoidance Propertiescited by 1
- 2025Neural Proofs for Sound Verification and Control of Complex Systemscited by 0
- 2025Neural Proofs for Sound Verification and Control of Complex Systems20 Dec 2025
- 2024Certified Policy Verification and Synthesis for MDPs under Distributional Reach-avoidance Properties7 May 2024
- 2023Compositional Policy Learning in Stochastic Control Systems with Formal Guarantees3 Dec 2023
- 2022Learning Control Policies for Stochastic Systems with Reach-avoid Guarantees11 Oct 2022
- 2022Learning Provably Stabilizing Neural Controllers for Discrete-Time Stochastic Systems11 Oct 2022
- 2022LCRL: Certified Policy Synthesis via Logically-Constrained Reinforcement Learning21 Sep 2022
- 2022Learning Stabilizing Policies in Stochastic Control Systems24 May 2022
- 2022SOCKS: A Stochastic Optimal Control and Reachability Toolbox Using Kernel Methods12 Mar 2022
- 2021Formal Verification of Unknown Dynamical Systems via Gaussian Process Regression31 Dec 2021
- 2021Stability Verification in Stochastic Control Systems via Neural Network Supermartingales17 Dec 2021
- 2021Scalable Synthesis of Verified Controllers in Deep Reinforcement Learning20 Apr 2021
- 2021Shielding Atari Games with Bounded Prescience20 Jan 2021