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.
Description
Measuring and Synthesizing Systems in Probabilistic Environments - Springer
%0 Book Section
%1 Chatterjee2010
%A Chatterjee, Krishnendu
%A Henzinger, ThomasA.
%A Jobstmann, Barbara
%A Singh, Rohit
%B Computer Aided Verification
%D 2010
%E Touili, Tayssir
%E Cook, Byron
%E Jackson, Paul
%I Springer Berlin Heidelberg
%K automata game probabilistic quantitative
%P 380-395
%R 10.1007/978-3-642-14295-6_34
%T Measuring and Synthesizing Systems in Probabilistic Environments
%U http://dx.doi.org/10.1007/978-3-642-14295-6_34
%V 6174
%X 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.
%@ 978-3-642-14294-9
@incollection{Chatterjee2010,
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.},
added-at = {2014-08-06T10:06:53.000+0200},
author = {Chatterjee, Krishnendu and Henzinger, ThomasA. and Jobstmann, Barbara and Singh, Rohit},
biburl = {https://www.bibsonomy.org/bibtex/2733595801555b0d9559b45ca250e1ccb/junkerm},
booktitle = {Computer Aided Verification},
description = {Measuring and Synthesizing Systems in Probabilistic Environments - Springer},
doi = {10.1007/978-3-642-14295-6_34},
editor = {Touili, Tayssir and Cook, Byron and Jackson, Paul},
file = {Chatterjee2010.pdf:Chatterjee2010.pdf:PDF},
groups = {public},
interhash = {b626300474f82b216f63e09b0b5be1d7},
intrahash = {733595801555b0d9559b45ca250e1ccb},
isbn = {978-3-642-14294-9},
keywords = {automata game probabilistic quantitative},
language = {English},
pages = {380-395},
publisher = {Springer Berlin Heidelberg},
series = {Lecture Notes in Computer Science},
timestamp = {2014-08-06T10:09:44.000+0200},
title = {Measuring and Synthesizing Systems in Probabilistic Environments},
url = {http://dx.doi.org/10.1007/978-3-642-14295-6_34},
username = {junkerm},
volume = 6174,
year = 2010
}