activity
20242026
collaborators

10 papers

cs.AI2026

Shielding for Higher-Order Safety

Filip Cano, Thomas A. Henzinger, Konstantin Kueffner

Safety shields are runtime enforcement mechanisms that restrict the actions of a controller to guarantee safety. Classical shields are usually synthesised for state predicates: the…

cs.FL2026

Monitoring Discounted Sum Properties

Filip Cano, Thomas A. Henzinger, Konstantin Kueffner +1

Runtime monitoring of quantitative signals faces a fundamental trade-off between volatility and over-aggregation: instantaneous observations are noisy, while long-run averages obsc…

cs.AI2026

Confidence Sequences for Online Statistical Model Checking of Markov Decision Processes

Konstantin Kueffner, Tobias Meggendorfer, Maximilian Weininger +1

Markov decision processes (MDPs) are a classic model of decision making under uncertainty, exhibiting both non-deterministic choice as well as probabilistic uncertainty. Traditiona…

cs.AI2026

Energy Shields for Fairness

Filip Cano, Thomas A. Henzinger, Konstantin Kueffner

Runtime fairness is not a one-time constraint but a dynamic property evaluated over a sequence of decisions. To ensure fairness at runtime, it is necessary to account for past deci…

cs.LO2025

Alignment Monitoring

Thomas A. Henzinger, Konstantin Kueffner, Vasu Singh +1

Formal verification provides assurances that a probabilistic system satisfies its specification--conditioned on the system model being aligned with reality. We propose alignment mo…

cs.AI2025

Algorithmic Fairness: A Runtime Perspective

Filip Cano, Thomas A. Henzinger, Konstantin Kueffner

Fairness in AI is traditionally studied as a static property evaluated once, over a fixed dataset. However, real-world AI systems operate sequentially, with outcomes and environmen…