Probabilistic model checking mainly concentrates on techniques for reasoning about the probabilities of certain path properties or expected values of certain random variables. For the quantitative system analysis, however, there is also another type of interesting performance measure, namely quantiles. A typical quantile query takes as input a lower probability bound p and a reachability property. The task is then to compute the minimal reward bound r such that with probability at least p the target set will be reached before the accumulated reward exceeds r. Quantiles are well-known from mathematical statistics, but to the best of our knowledge they have not been addressed by the model checking community so far. In this paper, we study the complexity of quantile queries for until properties in discrete-time finite-state Markov decision processes with non-negative rewards on states. We show that qualitative quantile queries can be evaluated in polynomial time and present an exponential algorithm for the evaluation of quantitative quantile queries. For the special case of Markov chains, we show that quantitative quantile queries can be evaluated in time polynomial in the size of the chain and the maximum reward.

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Published on 01/01/2013

Volume 2013, 2013

DOI: 10.1007/978-3-642-37075-5_23

Licence: CC BY-NC-SA license

Computer science • Discrete mathematics • Markov Reward Model • Markov decision process • Quantile queries • Probabilistic model checking • Software engineering • Markov chain • Markov decision process • Markow-Entscheidungsproblem • Belohnung • Quantil • Model Checking • Stochastische Modelle • Softwareentwicklung • Mathematical statistics • Model checking • Quantile • Random variable • Reachability • Time complexity

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