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  • SCC-Based Improved Reachability Analysis for Markov Decision Processes

    Author(s)
    Gui, L
    Dong, JS
    Sun, J
    Song, S
    Liu, Y
    Griffith University Author(s)
    Dong, Jin-Song
    Year published
    2014
    Metadata
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    Abstract
    Markov decision processes (MDPs) are extensively used to model systems with both probabilistic and nondeterministic behavior. The problem of calculating the probability of reaching certain system states (hereafter reachability analysis) is central to the MDP-based system analysis. It is known that existing approaches on reachability analysis for MDPs are often inefficient when a given MDP contains a large number of states and loops, especially with the existence of multiple probability distributions. In this work, we propose a method to eliminate strongly connected components (SCCs) in an MDP using a divide-and-conquer ...
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    Markov decision processes (MDPs) are extensively used to model systems with both probabilistic and nondeterministic behavior. The problem of calculating the probability of reaching certain system states (hereafter reachability analysis) is central to the MDP-based system analysis. It is known that existing approaches on reachability analysis for MDPs are often inefficient when a given MDP contains a large number of states and loops, especially with the existence of multiple probability distributions. In this work, we propose a method to eliminate strongly connected components (SCCs) in an MDP using a divide-and-conquer algorithm, and actively remove redundant probability distributions in the MDP based on the convex property. With the removal of loops and parts of probability distributions, the probabilistic reachability analysis can be accelerated, as evidenced by our experiment results.
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    Journal Title
    Lecture Notes in Computer Science
    Volume
    8829
    DOI
    https://doi.org/10.1007/978-3-319-11737-9_12
    Subject
    Software engineering not elsewhere classified
    Publication URI
    http://hdl.handle.net/10072/172894
    Collection
    • Journal articles

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