%0 Journal Article
%T Improving Probabilistic Bisimulation for MDPs Using Machine Learning
%J Mathematics Interdisciplinary Research
%I University of Kashan
%Z 2538-3639
%A Mohagheghi, Mohammadsadegh
%A Salehi, Khayyam
%D 2024
%\ 06/01/2024
%V 9
%N 2
%P 151-169
%! Improving Probabilistic Bisimulation for MDPs Using Machine Learning
%K Probabilistic bisimulation
%K Markov decision process
%K Model checking
%K Machine learning
%K Support Vector Machine
%R 10.22052/mir.2023.253367.1431
%X The utilization of model checking has been suggested as a formal verification technique for analyzing critical systems. However, the primary challenge in applying to complex systems is the state space explosion problem. To address this issue, bisimulation minimization has emerged as a prominent method for reducing the number of states in a system, aiming to overcome the difficulties associated with the state space explosion problem. For systems with stochastic behaviors, probabilistic bisimulation is employed to minimize a given model, obtaining its equivalent form with fewer states. In this paper, we propose a novel technique to partition the state space of a given probabilistic model to its bisimulation classes. This technique uses the PRISM program of a given model and constructs some small versions of the model to train a classifier. It then applies supervised machine learning techniques to approximately classify the related partition. The resulting partition is then used to accelerate the standard bisimulation technique, significantly reducing the running time of the method. The experimental results show that the approach can decrease significantly the running time compared to state-of-the-art tools.
%U https://mir.kashanu.ac.ir/article_114322_d21590eb7919d74be95308a790bc5283.pdf