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The Ins and Outs of the Probabilistic Model Checker MRMC
Probabilistic reachability for parametric Markov models
- E. M. Hahn, H. Hermanns, Lijun Zhang
- Computer Science, MathematicsInternational Journal on Software Tools for…
- 26 June 2009
It turns out that the bottleneck lies in the growth of the regular expression relative to the number of states (nΘ(log n), so the approach is to arrive at an effective method that avoids this blow up in most practical cases.
PARAM: A Model Checker for Parametric Markov Models
PARAM 1.0 is presented, a model checker for parametric discrete-time Markov chains (PMCs) that can evaluate temporal properties of PMCs and certain extensions of this class.
Model Checking Algorithms for CTMDPs
A new numerical approach based on uniformization is devised to compute time bounded reachability results for time dependent control strategies and experimental evidence is given showing the efficiency of the approach.
Model Repair for Markov Decision Processes
- Taolue Chen, E. M. Hahn, Tingting Han, M. Kwiatkowska, Hongyang Qu, Lijun Zhang
- BusinessInternational Symposium on Theoretical Aspects of…
- 1 July 2013
This paper first formulate a region-based approach, which yields an interval in which the minimal repair cost is contained, and also considers sampling based approaches, which are faster but unable to provide lower bounds on the repair cost.
Synthesis for PCTL in Parametric Markov Decision Processes
This paper studies the synthesis problem for PCTL in PMDPs, and synthesises the parameter valuations under which F is true, using existing decision procedures to check whether F holds on each of the Markov processes represented by the hyper-rectangle.
Omega-Regular Objectives in Model-Free Reinforcement Learning
- E. M. Hahn, Mateo Perez, S. Schewe, F. Somenzi, Ashutosh Trivedi, D. Wojtczak
- Computer ScienceTACAS
- 26 September 2018
This work presents a constructive reduction from the almost-sure satisfaction of \(\omega \)-regular objectives to analmost-sure reachability problem, and extends this technique to learning how to control an unknown model so that the chance of satisfying the objective is maximized.
Lazy Probabilistic Model Checking without Determinisation
- E. M. Hahn, Guangyuan Li, S. Schewe, Andrea Turrini, Lijun Zhang
- Computer ScienceCONCUR
- 11 November 2013
It is shown that full determinisation can be avoided: subset and breakpoint constructions suffice, and the approach is implemented---both explicit and symbolic versions---in a prototype tool.
A compositional modelling and analysis framework for stochastic hybrid systems
- E. M. Hahn, A. Hartmanns, H. Hermanns, J. Katoen
- Computer ScienceFormal Methods Syst. Des.
- 1 October 2013
HModest is presented, an extension to the Modest modelling language—which is originally designed for stochastic timed systems without complex continuous aspects—that adds differential equations and inclusions as an expressive way to describe the continuous system evolution.
JANI: Quantitative Model and Tool Interaction
- C. E. Budde, C. Dehnert, E. M. Hahn, A. Hartmanns, Sebastian Junges, Andrea Turrini
- Computer ScienceTACAS
- 22 April 2017
The Jani model format and tool interaction protocol is a metamodel based on networks of communicating automata and has been designed for ease of implementation without sacrificing readability, to provide a stable and uniform interface between tools such as model checkers, transformers, and user interfaces.