Stochastic Systems Divergence through Reinforcement Learning : How to use artificial intelligence to validate stochastic systems

Language: English

Published by LAP Lambert Academic Publishing, 2012

3847339710 / 9783847339717

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Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH

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nach der Bestellung gedruckt Neuware - Printed after ordering - Modelling real-life systems and phenomena using mathematical based formalisms is ubiquitous in science and engineering. The reason is that mathematics o er a suitable framework to carry out formal and rigorous analysis of these systems. For instance, in software engineering, formal methods are among the most e cient tools to identify aws in software. The behavior of many real-life systems is inherently stochastic which require stochastic models such as labelled Markov processes (LMPs), Markov decision processes (MDPs), predictive state representations (PSRs), etc. This thesis is about quantifying the di erence between stochastic systems. The important point of the thesis is that reinforcement learning (RL), a branch of arti cial intelligence particularly e cient in presence of uncertainty, can be used to quantify e ciently the divergence between stochastic systems. The key idea is to de ne an MDP out of the systems to be compared and then to interpret the optimal value of the MDP as the divergence between them. The most appealing feature of the proposed approach is that it does not rely on the knowledge of the internal structure of the systems.…

Seller Inventory # 9783847339717

Title
Stochastic Systems Divergence through Reinforcement Learning : How to use artificial intelligence to validate stochastic systems
Author
Sami Zhioua
Publisher
LAP Lambert Academic Publishing
Publication year
2012
Condition
Neu
Binding
Taschenbuch
Language
English
ISBN 10
3847339710
ISBN 13
9783847339717
Item weight
261 grams
Dimensions
22.00x15.00x0.90 cm

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