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.

许多重要的问题都涉及到不确定性下的决策--也就是说，根据往往是不完美的观察来选择行动，结果是未知的。自动决策支持系统的设计者必须考虑到不确定性的各种来源，同时平衡系统的多种目标。本书从计算的角度对不确定性下的决策挑战进行了介绍。它既介绍了决策模型和算法背后的理论，又收集了从语音识别到飞机防撞的应用实例。

本书侧重于设计决策代理的两种方法，即规划和强化学习，涵盖了概率模型，介绍了贝叶斯网络作为一种捕捉变量之间概率关系的图形模型；效用理论作为理解不确定条件下最优决策的框架；马尔科夫决策过程作为对顺序问题建模的方法；模型不确定性；状态不确定性；以及涉及多个互动代理的合作决策。一系列的应用显示了理论概念如何应用于基于属性的人员搜索系统、语音应用、避免碰撞和无人驾驶飞机的持续监视。

不确定条件下的决策》使用一致的符号统一了来自不同社区的研究，并可供事先接触过概率论和微积分的各工程学科的学生和研究人员使用。它可以作为计算机科学、航空航天和电气工程以及管理科学等领域的高级本科生和研究生的教材。它也将成为各学科研究人员的宝贵专业参考资料。