Research Paper on MODIFIED POLICY ITERATION FOR PROBABILISTIC MODEL CHECKING: IMPROVING

Description

Title: MODIFIED POLICY ITERATION FOR PROBABILISTIC MODEL CHECKING: IMPROVING

Abstract: Value iteration and policy iteration are well-known algorithms for the probabilistic model checking of Markov decision processes, as well as their modified versions. These methods have the drawback of typically taking a lot of time. To enhance the efficiency of iterative methods for probabilistic model checking, a number of techniques have been proposed; however, the running times of these techniques depend on the graphical structure of the model being used. Their performance may occasionally be inferior to that of conventional techniques. We suggest two new heuristics for speeding up the modified policy iteration method in this paper. We first establish a standard for measuring how useful each iteration of this method’s computations are. Our work’s primary contribution is the creation and application of a criterion for minimizing the number of modified policy iterations. As our second contribution, we suggest a fresh method for spotting pointless updates throughout each iteration. This approach shortens the computations’ running time by preventing pointless state updates. The proposed heuristics have been used on numerous common case studies and implemented in the PRISM model checker. We contrast the running times of our heuristics with those of earlier, more efficient, and standard methods. Our experimental findings demonstrate that our techniques produce a sizable speedup.

Keywords: probabilistic model checking, Markov decision processes, modified policy iteration, probabilistic reachability

Paper Quality: SCOPUS / Web of Science Level Research Paper

Paper type: Analysis Based Research Paper

Subject: Computer Science

Writer Experience: 20+ Years

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