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Found 5 papers, 1 papers with code
Formal Abstraction of General Stochastic Systems via Noise Partitioning
We introduce a general procedure for the finite abstraction of nonlinear stochastic systems with non-standard (e. g., non-affine, non-symmetric, non-unimodal) noise distributions for verification purposes.
Formal Verification of Unknown Dynamical Systems via Gaussian Process Regression
In this article, we develop a framework for verifying partially-observable, discrete-time dynamical systems with unmodelled dynamics against temporal logic specifications from a given input-output dataset.
Synergistic Offline-Online Control Synthesis via Local Gaussian Process Regression
The online controller may improve the baseline guarantees since it avoids the discretization error and reduces regression error as new data is collected.
Strategy Synthesis for Partially-known Switched Stochastic Systems
We present a data-driven framework for strategy synthesis for partially-known switched stochastic systems.
Factorized Machine Self-Confidence for Decision-Making Agents
Markov decision processes underlie much of the theory of reinforcement learning, and are commonly used for planning and decision making under uncertainty in robotics and autonomous systems.
