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

Zhioua, Sami

 
9783847339717: Stochastic Systems Divergence through Reinforcement Learning: How to use artificial intelligence to validate stochastic systems
Couverture souple
ISBN 10 : 3847339710 ISBN 13 : 9783847339717
Editeur : LAP LAMBERT Academic Publishing, 2012

Synopsis

Modelling real-life systems and phenomena using mathematical based formalisms is ubiquitous in science and engineering. The reason is that mathematics offer a suitable framework to carry out formal and rigorous analysis of these systems. For instance, in software engineering, formal methods are among the most efficient tools to identify flaws 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 difference between stochastic systems. The important point of the thesis is that reinforcement learning (RL), a branch of artificial intelligence particularly efficient in presence of uncertainty, can be used to quantify efficiently the divergence between stochastic systems. The key idea is to define 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.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

Présentation de l'éditeur

Modelling real-life systems and phenomena using mathematical based formalisms is ubiquitous in science and engineering. The reason is that mathematics offer a suitable framework to carry out formal and rigorous analysis of these systems. For instance, in software engineering, formal methods are among the most efficient tools to identify flaws 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 difference between stochastic systems. The important point of the thesis is that reinforcement learning (RL), a branch of artificial intelligence particularly efficient in presence of uncertainty, can be used to quantify efficiently the divergence between stochastic systems. The key idea is to define 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.

Biographie de l'auteur

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.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
LAP LAMBERT Academic Publishing
Date d'édition
2012
Langue
anglais
ISBN 10 
3847339710
ISBN 13 
9783847339717
Reliure
Broché
Nombre de pages
164
Coordonnées du fabricant
non disponible
Personne responsable
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