Author

Jan Křetinský

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Preprint Jul 2026

dtControl2+$\varepsilon$: Trading Optimality for Explainability in MDPs via Decision Trees

Over the past decade, decision trees have been used to represent controllers (a.k.a. policies) in an explainable way, with dtControl2 as a current state-of-the-art tool. However, for systems that are large or have many corner cases, even such representations tend to be too complex and not human-comprehensible. Unfortun...

Tereza Kinská, Jan Křetinský, Tobias Meggendorfer et al. · 0 citations

Preprint Aug 2026

Quantifying Risk Under Evolving Uncertainty: Belief-Dependent Robustness for Safe Sequential Decision Making

RATTL targets runtime safety for agents, including LLM-based systems, acting under uncertainty, and proves a Safety Sandwich: the RATTL value lies between the uninformed robust value and the full- knowledge optimum, with a gap that vanishes as the posterior concentrates.

D. Ganguly, Jan Křetinský · 0 citations

#artificial intelligence Preprint Aug 2026

Robust Risk Under Evolving Uncertainty: A Wasserstein Counterpart of the Entropic Value-at-Risk

An agent still learning its environment should be cautious while ignorant and bold once confident. The entropic value-at-risk captures this through a robust-optimization identity---a confidence level fixes the radius of a relative-entropy ball of alternative models---but that ball cannot reach catastrophes the nominal...

D. Ganguly, Jan Křetinský · 0 citations