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Stochastic Systems Divergence Through Reinforcement Learning

Sami Zhioua

Published by LAP Lambert Academic Publishing
ISBN 10: 3847339710 / ISBN 13: 9783847339717
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Bibliographic Details


Title: Stochastic Systems Divergence Through ...

Publisher: LAP Lambert Academic Publishing

Binding: Paperback

Book Condition: New

Book Type: Paperback

Description:

Paperback. 164 pages. Dimensions: 8.7in. x 5.9in. x 0.4in.Modelling real-life systems and phenomena using mathematical based formalisms is ubiquitous in science and engineering. The reason is that mathematics oer a suitable framework to carry out formal and rigorous analysis of these systems. For instance, in software engineering, formal methods are among the most ecient 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 dierence between stochastic systems. The important point of the thesis is that reinforcement learning (RL), a branch of articial intelligence particularly ecient in presence of uncertainty, can be used to quantify eciently the divergence between stochastic systems. The key idea is to dene 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. This item ships from multiple locations. Your book may arrive from Roseburg,OR, La Vergne,TN. Bookseller Inventory # 9783847339717

About this title:

Synopsis: 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.

About the Author: Dr. Sami Zhioua is assistant professor at the Information and Computer Science department of KFUPM. Before, he was a post-doctoral research and teaching fellow at McGill University, Canada. He graduated from Laval University, Canada (Ph.D. 2008 and M.Sc. 2003). His main research fields are Information Security and Reinforcement Learning.

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