Unbounded-2-bounded: a two-phase approximation for model checking unbounded until properties of probabilistic systems

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2011-01-01
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Jennings, Paul
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Samik Basu
Arka P. Ghosh
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Computer Science

Computer Science—the theory, representation, processing, communication and use of information—is fundamentally transforming every aspect of human endeavor. The Department of Computer Science at Iowa State University advances computational and information sciences through; 1. educational and research programs within and beyond the university; 2. active engagement to help define national and international research, and 3. educational agendas, and sustained commitment to graduating leaders for academia, industry and government.

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The Computer Science Department was officially established in 1969, with Robert Stewart serving as the founding Department Chair. Faculty were composed of joint appointments with Mathematics, Statistics, and Electrical Engineering. In 1969, the building which now houses the Computer Science department, then simply called the Computer Science building, was completed. Later it was named Atanasoff Hall. Throughout the 1980s to present, the department expanded and developed its teaching and research agendas to cover many areas of computing.

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1969-present

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Abstract

We have developed a new approximate probabilistic model checking method for untimed properties in probabilistic systems, expressed in a probabilistic temporal logic. In contrast to existing methods, this method does not require the untimed until properties to be bounded, where the bound refers to the number of discrete steps in the system required to verify the until property. The method consists of two phases. In the first phase, a suitable system- and property-dependent bound is obtained automatically. In the second phase, the probability of satisfying the bounded until property is computed as the estimate of the probability of satisfying the original unbounded until property. Both phases require only verification of bounded until properties, which can be effectively performed by existing simulation-based methods. We prove the correctness of the proposed two-phase method and present its optimized implementation in the widely used PRISM model checking engine. We compare this implementation with other sampling-based model checking techniques and show that for several models these existing tools fail to compute the result, while the two-phase method successfully computes the result efficiently with respect to time and space.

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Sat Jan 01 00:00:00 UTC 2011