CEGAR for compositional analysis of qualitative properties in Markov decision processes

2015 ◽  
Vol 47 (2) ◽  
pp. 230-264
Author(s):  
Krishnendu Chatterjee ◽  
Martin Chmelík ◽  
Przemysław Daca
Author(s):  
Mohammadsadegh Mohagheghi ◽  
Khayyam Salehi

<span>Probabilistic model checking is a formal verification method, which is used to guarantee the correctness of the computer systems with stochastic behaviors. Reachability probabilities are the main class of properties that are proposed in probabilistic model checking. Some graph-based pre-computation can determine those states for which the reachability probability is exactly zero or one. Iterative numerical methods are used to compute the reachability probabilities for the remaining states. In this paper, we focus on the graph-based pre-computations and propose a heuristic to improve the performance of these pre-computations. The proposed heuristic approximates the set of states that are computed in the standard pre-computation methods. The experiments show that the proposed heuristic can compute a main part of the expected states, while reduces the running time by several orders of magnitude.</span>


1983 ◽  
Vol 20 (04) ◽  
pp. 835-842
Author(s):  
David Assaf

The paper presents sufficient conditions for certain functions to be convex. Functions of this type often appear in Markov decision processes, where their maximum is the solution of the problem. Since a convex function takes its maximum at an extreme point, the conditions may greatly simplify a problem. In some cases a full solution may be obtained after the reduction is made. Some illustrative examples are discussed.


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