Convex-Analytic View of Query Selection for Safety Verification of Markov Decision Processes
Safety verification of a Markov decision process (MDP) asks whether the probabilities of reaching several failure states jointly lie in a convex safe region under every scheduler. Watanabe and Unno (2026) prove safety by refining an outer approximation of the failure probabilities, where each query bounds a nonnegative weighted sum of the probabilities. Their rule selects weights by the largest Euclidean violation. We present a unified framework to measure violation in an explicitly chosen error unit: directions are scored by the support-function gap between the outer approximation and the safe region, normalized by the support function of the unit. We show that the maximal score, when po...
| Authors | Han Bao, Shinsaku Sakaue, Kazuki Watanabe |
|---|---|
| Primary category | math.OC |
| Published | 13 October 2026 |
| Last updated at source | 13 October 2026 |
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