Decision making under uncertainty. Theory and application. With contributions from Christopher Amato, Girish Chowdhary, Jonathan P. How, Hayley J. Davison Reynolds, Jason R. Thornton, Pedro A. Torres-Carrasquillo, N. Kemal Üre and John Vian (Q2794334)

The book consists of an introduction and two parts, on Theory and Applications, respectively. NEWLINEThe first part, except for one chapter, is written by M. J. Kochenderfer, and the second part is written by the mentioned author and the other experts in specific applications. NEWLINEStudents in computer and management sciences, and in aerospace and electrical engineering, are meant to be the main audience of the book. NEWLINEThe authors claim that the book ``provides an introduction to decision making under uncertainty from computational perspective''. NEWLINEIn the first part, the basics of rational decision theory are considered. NEWLINEProbabilistic models are briefly presented by the concepts of comparative subjective probability, probability distributions, Bayesian networks, inference, and estimation of parameters via learning methods. NEWLINEIn Chapter 3, which is focussed on decision problems, the basic ideas of utility and game theory are introduced. NEWLINEThe next chapter is on sequential decision making. NEWLINEChapter 5, ``Model uncertainty'', is focussed on the methods of reinforcement learning. NEWLINEIn Chapter 6 the methods of decision making considered in the previous chapters are generalized to the cases with state uncertainty, and the partially observable Markov decision process is introduced. NEWLINEChapter 7, written by C. Amato, is on cooperative decision making. NEWLINEIn the second part of the book various applications are described: probabilistic surveillance video search, speech recognition and speaker identification, aircraft flight planning and collision avoidance, and integrating automation with humans.