13 citations · 26 across the 9 of their papers we have counts for
11 papers
Using Unit Propagation with Universal Reduction in DQBF Preprocessing
Ralf Wimmer, Ming-Yi Hu
Several effective preprocessing techniques for Boolean formulas with and without quantifiers use unit propagation to simplify the formula. Among these techniques are vivification,…
Strengthening Deterministic Policies for POMDPs
Leonore Winterer, Ralf Wimmer, Nils Jansen +1
The synthesis problem for partially observable Markov decision processes (POMDPs) is to compute a policy that satisfies a given specification. Such policies have to take the full e…
Counterexample-Guided Strategy Improvement for POMDPs Using Recurrent Neural Networks
Steven Carr, Nils Jansen, Ralf Wimmer +3
We study strategy synthesis for partially observable Markov decision processes (POMDPs). The particular problem is to determine strategies that provably adhere to (probabilistic) t…
Human-in-the-Loop Synthesis for Partially Observable Markov Decision Processes
Steven Carr, Nils Jansen, Ralf Wimmer +2
We study planning problems where autonomous agents operate inside environments that are subject to uncertainties and not fully observable. Partially observable Markov decision proc…
Permissive Finite-State Controllers of POMDPs using Parameter Synthesis
Sebastian Junges, Nils Jansen, Ralf Wimmer +4
We study finite-state controllers (FSCs) for partially observable Markov decision processes (POMDPs) that are provably correct with respect to given specifications. The key insight…
Strategy Synthesis in POMDPs via Game-Based Abstractions
Leonore Winterer, Sebastian Junges, Ralf Wimmer +4
We study synthesis problems with constraints in partially observable Markov decision processes (POMDPs), where the objective is to compute a strategy for an agent that is guarantee…
MeGARA: Menu-based Game Abstraction and Abstraction Refinement of Markov Automata
Bettina Braitling, Luis María Ferrer Fioriti, Hassan Hatefi +3
Markov automata combine continuous time, probabilistic transitions, and nondeterminism in a single model. They represent an important and powerful way to model a wide range of comp…
Accelerating Parametric Probabilistic Verification
Nils Jansen, Florian Corzilius, Matthias Volk +4
We present a novel method for computing reachability probabilities of parametric discrete-time Markov chains whose transition probabilities are fractions of polynomials over a set…
High-level Counterexamples for Probabilistic Automata
Ralf Wimmer, Nils Jansen, Erika Ábrahám +1
Providing compact and understandable counterexamples for violated system properties is an essential task in model checking. Existing works on counterexamples for probabilistic syst…
Transient Reward Approximation for Continuous-Time Markov Chains
Ernst Moritz Hahn, Holger Hermanns, Ralf Wimmer +1
We are interested in the analysis of very large continuous-time Markov chains (CTMCs) with many distinct rates. Such models arise naturally in the context of reliability analysis,…
The COMICS Tool - Computing Minimal Counterexamples for Discrete-time Markov Chains
Nils Jansen, Erika Ábrahám, Maik Scheffler +5
This report presents the tool COMICS, which performs model checking and generates counterexamples for DTMCs. For an input DTMC, COMICS computes an abstract system that carries the…