English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

Closed-loop verification of cyber-physical systems with neural networkcontrollers offers strong safety guarantees under certain assumptions. It is,however, difficult to determine whether these guarantees apply at run timebecause verification assumptions may be violated. To predict safety violationsin a verified system, we propose a three-step confidence composition (CoCo)framework for monitoring verification assumptions. First, we represent thesufficient condition for verified safety with a propositional logical formulaover assumptions. Second, we build calibrated confidence monitors that evaluatethe probability that each assumption holds. Third, we obtain the confidence inthe verification guarantees by composing the assumption monitors using acomposition function suitable for the logical formula. Our CoCo frameworkprovides theoretical bounds on the calibration and conservatism ofcompositional monitors. Two case studies show that compositional monitors arecalibrated better than their constituents and successfully predict safetyviolations.

Confidence Composition for Monitors of Verification Assumptions