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Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
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Decision Making Under Uncertainty - Theory and Application (Hardcover)
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Decision Making Under Uncertainty - Theory and Application (Hardcover)
Series: MIT Lincoln Laboratory Series
Expected to ship within 9 - 17 working days
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An introduction to decision making under uncertainty from a
computational perspective, covering both theory and applications
ranging from speech recognition to airborne collision avoidance.
Many important problems involve decision making under
uncertainty-that is, choosing actions based on often imperfect
observations, with unknown outcomes. Designers of automated
decision support systems must take into account the various sources
of uncertainty while balancing the multiple objectives of the
system. This book provides an introduction to the challenges of
decision making under uncertainty from a computational perspective.
It presents both the theory behind decision making models and
algorithms and a collection of example applications that range from
speech recognition to aircraft collision avoidance. Focusing on two
methods for designing decision agents, planning and reinforcement
learning, the book covers probabilistic models, introducing
Bayesian networks as a graphical model that captures probabilistic
relationships between variables; utility theory as a framework for
understanding optimal decision making under uncertainty; Markov
decision processes as a method for modeling sequential problems;
model uncertainty; state uncertainty; and cooperative decision
making involving multiple interacting agents. A series of
applications shows how the theoretical concepts can be applied to
systems for attribute-based person search, speech applications,
collision avoidance, and unmanned aircraft persistent surveillance.
Decision Making Under Uncertainty unifies research from different
communities using consistent notation, and is accessible to
students and researchers across engineering disciplines who have
some prior exposure to probability theory and calculus. It can be
used as a text for advanced undergraduate and graduate students in
fields including computer science, aerospace and electrical
engineering, and management science. It will also be a valuable
professional reference for researchers in a variety of disciplines.
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