ARCH-COMP23 Category Report: Stochastic Models

Abstract<br/>This report is concerned with a friendly competition for formal verification and policy synthesis of stochastic models. The main goal of the report is to introduce new benchmarks and their properties within this category and recommend next steps toward next year’s edition of the competition. Given that the tools for stochastic...

ARCH-COMP22 Category Report: Stochastic Models

This report presents the results of a friendly competition for formal verification and policy synthesis of stochastic models. It also introduces new benchmarks and their properties within this category and recommends next steps for this category towards next year’s edition of the competition. In comparison with tools on non-probabilistic models,...

Data-Driven Stability Verification of Homogeneous Nonlinear Systems with Unknown Dynamics

In this work, we propose a data-driven approach for the stability analysis of discrete-time homogeneous nonlinear systems with unknown models. The proposed framework is based on constructing Lyapunov functions via a set of data, collected from trajectories of unknown systems, while providing an a-priori guaranteed confidence on the stability of...

ARCH-COMP20 Category Report: Stochastic Models

This report presents the results of a friendly competition for formal verification and<br/>policy synthesis of stochastic models. It also introduces new benchmarks within this<br/>category, and recommends next steps for this category towards next year's edition of the competition. The friendly competition took place as part of the workshop...