Festschrift 25 Jahre Fachgebiet Maschinenelemente und Mechanik, Technische Hochschule Darmstadt

Festschrift 25 Jahre Fachgebiet Maschinenelemente und Mechanik, Technische Hochschule Darmstadt Leiter Prof. Dr.-Ing. Walter Raab. Kolloquium am 13. September 1991, Darmstad

Partial Order Reduction for Probabilistic Branching Time

In the past, partial order reduction has been used successfully to combat the state explosion problem in the context of model checking for non-probabilistic systems. For both linear time and branching time specifications, methods have been developed to apply partial order reduction in the context of model checking. Only recently, results were published that give criteria on applying partial order reduction for verifying quantitative linear time properties for probabilistic systems. This paper presents partial order reduction criteria for Markov decision processes and branching time properties, such as formulas of probabilistic computation tree logic. Moreover, we provide a comparison of the results established so far about reduction conditions for Markov decision processes

Festschrift 25 Jahre Fachgebiet Maschinenelemente und Mechanik, Technische Hochschule Darmstadt

Festschrift 25 Jahre Fachgebiet Maschinenelemente und Mechanik, Technische Hochschule Darmstadt Leiter Prof. Dr.-Ing. Walter Raab. Kolloquium am 13. September 1991, Darmstad

Weighted versus Probabilistic Logics

While a mature theory around logics such as MSO, LTL, and CTL has been developed in the pure boolean setting of finite automata, weighted automata lack such a natural connection with (temporal) logic and related verification algorithms. In this paper, we will identify weighted versions of MSO and CTL that generalize the classical logics and even other quantitative extensions such as probabilistic CTL. We establish expressiveness results on our logics giving translations from weighted and probabilistic CTL into weighted MSO

Automated Verification Techniques for Probabilistic Systems

Abstract. This tutorial provides an introduction to probabilistic model checking, a technique for automatically verifying quantitative properties of probabilistic systems. We focus on Markov decision processes (MDPs), which model both stochastic and nondeterministic behaviour. We describe methods to analyse a wide range of their properties, including specifications in the temporal logics PCTL and LTL, probabilistic safety properties and cost- or reward-based measures. We also discuss multiobjective probabilistic model checking, used to analyse trade-offs between several different quantitative properties. Applications of the techniques in this tutorial include performance and dependability analysis of networked systems, communication protocols and randomised distributed algorithms. Since such systems often comprise several components operating in parallel, we also cover techniques for compositional modelling and verification of multi-component probabilistic systems. Finally, we describe three large case studies which illustrate practical applications of the various methods discussed in the tutorial.