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Found 8 papers, 3 papers with code
A Stability-Based Abstraction Framework for Reach-Avoid Control of Stochastic Dynamical Systems with Unknown Noise Distributions
To address this issue, we propose a novel abstraction scheme for stochastic linear systems that exploits the system's stability to obtain significantly smaller abstract models.
Correct-by-Construction Control for Stochastic and Uncertain Dynamical Models via Formal Abstractions
Such autonomous systems are naturally modeled as stochastic dynamical models.
Efficient Sensitivity Analysis for Parametric Robust Markov Chains
As our main contribution, we present an efficient method to compute these partial derivatives.
Decision-Making Under Uncertainty: Beyond Probabilities
In this paper, the focus is on the uncertainty that goes beyond this classical interpretation, particularly by employing a clear distinction between aleatoric and epistemic uncertainty.
Robust Control for Dynamical Systems With Non-Gaussian Noise via Formal Abstractions
This iMDP is, with a user-specified confidence probability, robust against uncertainty in the transition probabilities, and the tightness of the probability intervals can be controlled through the number of samples.
Formal Controller Synthesis for Markov Jump Linear Systems with Uncertain Dynamics
We consider the cases where the transition probabilities of this MDP are either known up to an interval or completely unknown.
Probabilities Are Not Enough: Formal Controller Synthesis for Stochastic Dynamical Models with Epistemic Uncertainty
Stochastic noise causes aleatoric uncertainty, whereas imprecise knowledge of model parameters leads to epistemic uncertainty.
Correct-by-construction reach-avoid control of partially observable linear stochastic systems
By construction, any policy on the abstraction can be refined into a piecewise linear feedback controller for the LTI system.
