8 citations · 17 across the 5 of their papers we have counts for
5 papers
Stopping Criteria for Value Iteration on Stochastic Games with Quantitative Objectives
Jan Křetínský, Tobias Meggendorfer, Maximilian Weininger
A classic solution technique for Markov decision processes (MDP) and stochastic games (SG) is value iteration (VI). Due to its good practical performance, this approximative approa…
A Practitioner's Guide to MDP Model Checking Algorithms
Arnd Hartmanns, Sebastian Junges, Tim Quatmann +1
Model checking undiscounted reachability and expected-reward properties on Markov decision processes (MDPs) is key for the verification of systems that act under uncertainty. Popul…
Algebraically Explainable Controllers: Decision Trees and Support Vector Machines Join Forces
Florian Jüngermann, Jan Křetínský, Maximilian Weininger
Recently, decision trees (DT) have been used as an explainable representation of controllers (a.k.a. strategies, policies, schedulers). Although they are often very efficient and p…
Optimistic and Topological Value Iteration for Simple Stochastic Games
Muqsit Azeem, Alexandros Evangelidis, Jan Křetínský +2
While value iteration (VI) is a standard solution approach to simple stochastic games (SSGs), it suffered from the lack of a stopping criterion. Recently, several solutions have ap…
Satisfiability Bounds for -Regular Properties in Bounded-Parameter Markov Decision Processes
Jan Křetínský, Tobias Meggendorfer, Maximilian Weininger
We consider the problem of computing minimum and maximum probabilities of satisfying an -regular property in a bounded-parameter Markov decision process (BMDP). BMDP arise from…