Decision Making Under Uncertainty: Theory And Application (mit Lincoln Laboratory Series)
by Mykel J. Kochenderfer /
2015 / English / PDF
5.9 MB Download
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.
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.
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
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.
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.