22 citations · 25 across the 5 of their papers we have counts for
7 papers · 1 filter
J-P: MDP. FP. PP.: Characterizing Total Expected Rewards in Markov Decision Processes as Least Fixed Points with an Application to Operational Semantics of Probabilistic Programs (Technical Report)
Kevin Batz, Benjamin Lucien Kaminski, Christoph Matheja +1
Markov decision processes (MDPs) with rewards are a widespread and well-studied model for systems that make both probabilistic and nondeterministic choices. A fundamental result ab…
A Calculus for Amortized Expected Runtimes
Kevin Batz, Benjamin Lucien Kaminski, Joost-Pieter Katoen +2
We develop a weakest-precondition-style calculus à la Dijkstra for reasoning about amortized expected runtimes of randomized algorithms with access to dynamic memory - the $\textsf…
Foundations for Entailment Checking in Quantitative Separation Logic (extended version)
Kevin Batz, Ira Fesefeldt, Marvin Jansen +4
Quantitative separation logic (QSL) is an extension of separation logic (SL) for the verification of probabilistic pointer programs. In QSL, formulae evaluate to real numbers inste…
PrIC3: Property Directed Reachability for MDPs
Kevin Batz, Sebastian Junges, Benjamin Lucien Kaminski +3
IC3 has been a leap forward in symbolic model checking. This paper proposes PrIC3 (pronounced pricy-three), a conservative extension of IC3 to symbolic model checking of MDPs. Our…
A Pre-Expectation Calculus for Probabilistic Sensitivity
Alejandro Aguirre, Gilles Barthe, Justin Hsu +3
Sensitivity properties describe how changes to the input of a program affect the output, typically by upper bounding the distance between the outputs of two runs by a monotone func…
Inferring Covariances for Probabilistic Programs
Benjamin Lucien Kaminski, Joost-Pieter Katoen, Christoph Matheja
We study weakest precondition reasoning about the (co)variance of outcomes and the variance of run-times of probabilistic programs with conditioning. For outcomes, we show that app…