Configuration of inter-process communication with probabilistic model checking

2019 ◽  
Vol 21 (6) ◽  
pp. 651-666 ◽  
Author(s):  
Linda Herrmann ◽  
Martin Küttler ◽  
Tobias Stumpf ◽  
Christel Baier ◽  
Hermann Härtig ◽  
...  
Keyword(s):  
Model Checking ◽  
Probabilistic Model ◽  
Probabilistic Model Checking ◽  
Process Communication ◽  
Inter Process Communication

scholarly journals Probabilistic Model Checking and Non-standard Multi-objective Reasoning

2014 ◽  
pp. 1-16 ◽  
Author(s):  
Christel Baier ◽  
Clemens Dubslaff ◽  
Sascha Klüppelholz ◽  
Marcus Daum ◽  
Joachim Klein ◽  
...  
Keyword(s):  
Model Checking ◽  
Probabilistic Model ◽  
Probabilistic Model Checking ◽  
Multi Objective ◽  
Objective Reasoning

scholarly journals A Railway Scheduling Method using Probabilistic Model Checking

2019 ◽  
Vol 9 (1) ◽  
pp. 6694-6698
Keyword(s):  
Model Checking ◽  
Probabilistic Model ◽  
Probabilistic Model Checking ◽  
Railway Transportation ◽  
Railway Network ◽  
Double Line ◽  
Arrival Times ◽  
Scheduling Method ◽  
Prism Model ◽  
The Impact

Trains scheduling is an important problem in railway transportation. Many companies use fixed train timetabling to handle this problem. Train delays can affect the pre-defined timetables and postpone destination arrival times. Besides, delay propagation may affect other trains and degrade the performance of a railway network. An optimal timetable minimizes the total propagated delays in a network. In this paper, we propose a new approach to compute the expected propagated delays in a railway network. As the main contribution of the work, we use Discrete-time Markov chains to model a railway network with a fixed timetable and use probabilistic model checking to approximate the expected delays and the probability of reaching destinations with a desired delay. We use PRISM model checker to apply our approach for analyzing the impact of different train scheduling in double line tracks.

Evaluating probabilistic model checking tools for verification of robot control policies

2016 ◽  
Vol 29 (2) ◽  
pp. 287-299 ◽  
Author(s):  
Shashank Pathak ◽  
Luca Pulina ◽  
Armando Tacchella
Keyword(s):  
Model Checking ◽  
Probabilistic Model ◽  
Robot Control ◽  
Probabilistic Model Checking ◽  
Control Policies

scholarly journals Advances in Symbolic Probabilistic Model Checking with PRISM

2016 ◽  
pp. 349-366 ◽  
Author(s):  
Joachim Klein ◽  
Christel Baier ◽  
Philipp Chrszon ◽  
Marcus Daum ◽  
Clemens  Dubslaff ◽  
...  
Keyword(s):  
Model Checking ◽  
Probabilistic Model ◽  
Probabilistic Model Checking

Quantitative Reasoning About Dependability in Event-B

2012 ◽  
pp. 459-472 ◽  
Author(s):  
Anton Tarasyuk ◽  
Elena Troubitsyna ◽  
Linas Laibinis
Keyword(s):  
Model Checking ◽  
Probabilistic Model ◽  
Probabilistic Reasoning ◽  
Logical Reasoning ◽  
Quantitative Reasoning ◽  
Probabilistic Model Checking ◽  
Functional Correctness

Formal refinement-based approaches have proved their worth in verifying system correctness. Often, besides ensuring functional correctness, we also need to quantitatively demonstrate that the desired level of dependability is achieved. However, the existing refinement-based frameworks do not provide sufficient support for quantitative reasoning. In this chapter, we show how to use probabilistic model checking to verify probabilistic refinement of Event-B models. Such integration allows us to combine logical reasoning about functional correctness with probabilistic reasoning about reliability.

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