Items related to Continuous-Time Markov Decision Processes: Theory and...
Synopsis
Continuous-time Markov decision processes (MDPs), also known as controlled Markov chains, are used for modeling decision-making problems that arise in operations research (for instance, inventory, manufacturing, and queueing systems), computer science, communications engineering, control of populations (such as fisheries and epidemics), and management science, among many other fields. This volume provides a unified, systematic, self-contained presentation of recent developments on the theory and applications of continuous-time MDPs. The MDPs in this volume include most of the cases that arise in applications, because they allow unbounded transition and reward/cost rates. Much of the material appears for the first time in book form.
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About the Author
Onésimo Hernández-Lerma received the Science and Arts National Award from the Government of MEXICO in 2001, an honorary doctorate from the University of Sonora in 2003, and the Scopus Prize from Elsevier in 2008. Xianping Guo received the He-Pan-Qing-Yi Best Paper Award from the 7th Word Congress on Intelligent Control and Automation in 2008.
Review
From the reviews:
“The book consists of 12 chapters. ... this is the first monograph on continuous-time Markov decision process. ... This is an important book written by leading experts on a mathematically rich topic which has many applications to engineering, business, and biological problems. ... scholars and students interested in developing the theory of continuous-time Markov decision Processes or working on their applications should have this book.” (E. A. Feinberg, Mathematical Reviews, Issue 2011 b)"About this title" may belong to another edition of this title.
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