2 research outputs found
Statistical Verification of Autonomous Systems using Surrogate Models and Conformal Inference
In this paper, we propose conformal inference based approach for statistical
verification of CPS models. Cyber-physical systems (CPS) such as autonomous
vehicles, avionic systems, and medical devices operate in highly uncertain
environments. This uncertainty is typically modeled using a finite number of
parameters or input signals. Given a system specification in Signal Temporal
Logic (STL), we would like to verify that for all (infinite) values of the
model parameters/input signals, the system satisfies its specification.
Unfortunately, this problem is undecidable in general. {\em Statistical model
checking} (SMC) offers a solution by providing guarantees on the correctness of
CPS models by statistically reasoning on model simulations. We propose a new
approach for statistical verification of CPS models for user-provided
distribution on the model parameters. Our technique uses model simulations to
learn {\em surrogate models}, and uses {\em conformal inference} to provide
probabilistic guarantees on the satisfaction of a given STL property.
Additionally, we can provide prediction intervals containing the quantitative
satisfaction values of the given STL property for any user-specified confidence
level. We also propose a refinement procedure based on Gaussian Process
(GP)-based surrogate models for obtaining fine-grained probabilistic guarantees
over sub-regions in the parameter space. This in turn enables the CPS designer
to choose assured validity domains in the parameter space for safety-critical
applications. Finally, we demonstrate the efficacy of our technique on several
CPS models
Model-Free Reinforcement Learning for Stochastic Games with Linear Temporal Logic Objectives
We study the problem of synthesizing control strategies for Linear Temporal
Logic (LTL) objectives in unknown environments. We model this problem as a
turn-based zero-sum stochastic game between the controller and the environment,
where the transition probabilities and the model topology are fully unknown.
The winning condition for the controller in this game is the satisfaction of
the given LTL specification, which can be captured by the acceptance condition
of a deterministic Rabin automaton (DRA) directly derived from the LTL
specification. We introduce a model-free reinforcement learning (RL)
methodology to find a strategy that maximizes the probability of satisfying a
given LTL specification when the Rabin condition of the derived DRA has a
single accepting pair. We then generalize this approach to LTL formulas for
which the Rabin condition has a larger number of accepting pairs, providing a
lower bound on the satisfaction probability. Finally, we illustrate
applicability of our RL method on two motion planning case studies