scholarly journals Efficient Probabilistic Model Checking on General Purpose Graphics Processors

2009 ◽  
pp. 32-49 ◽  
Author(s):  
Dragan Bošnački ◽  
Stefan Edelkamp ◽  
Damian Sulewski
Keyword(s):  
Model Checking ◽  
Probabilistic Model ◽  
General Purpose ◽  
Probabilistic Model Checking ◽  
Graphics Processors

Parallel probabilistic model checking on general purpose graphics processors

2010 ◽  
Vol 13 (1) ◽  
pp. 21-35 ◽  
Author(s):  
Dragan Bošnački ◽  
Stefan Edelkamp ◽  
Damian Sulewski ◽  
Anton Wijs
Keyword(s):  
Model Checking ◽  
Probabilistic Model ◽  
General Purpose ◽  
Probabilistic Model Checking ◽  
Graphics Processors

Ontology-Mediated Probabilistic Model Checking

2019 ◽  
pp. 194-211 ◽  
Author(s):  
Clemens Dubslaff ◽  
Patrick Koopmann ◽  
Anni-Yasmin Turhan
Keyword(s):  
Model Checking ◽  
Probabilistic Model ◽  
Probabilistic Model Checking

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
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