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Measuring and Synthesizing Systems in Probabilistic Environments

, , , and . Computer Aided Verification, volume 6174 of Lecture Notes in Computer Science, Springer Berlin Heidelberg, (2010)
DOI: 10.1007/978-3-642-14295-6_34

Abstract

Often one has a preference order among the different systems that satisfy a given specification. Under a probabilistic assumption about the possible inputs, such a preference order is naturally expressed by a weighted automaton, which assigns to each word a value, such that a system is preferred if it generates a higher expected value. We solve the following optimal-synthesis problem: given an omega-regular specification, a Markov chain that describes the distribution of inputs, and a weighted automaton that measures how well a system satisfies the given specification under the given input assumption, synthesize a system that optimizes the measured value.

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Measuring and Synthesizing Systems in Probabilistic Environments - Springer

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