Decision Making Under Uncertainty: Theory And Application

Decision Making Under Uncertainty: Theory And Application
by Mykel J. Kochenderfer / / / PDF


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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

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