Algorithms for Decision Making (Hardcover)

A broad introduction to algorithms for decision making under uncertainty, introducing the underlying mathematical problem formulations and the algorithms for solving them.

Automated decision-making systems or decision-support systems--used in applications that range from aircraft collision avoidance to breast cancer screening--must be designed to account for various sources of uncertainty while carefully balancing multiple objectives. This textbook provides a broad introduction to algorithms for decision making under uncertainty, covering the underlying mathematical problem formulations and the algorithms for solving them.

The book first addresses the problem of reasoning about uncertainty and objectives in simple decisions at a single point in time, and then turns to sequential decision problems in stochastic environments where the outcomes of our actions are uncertain. It goes on to address model uncertainty, when we do not start with a known model and must learn how to act through interaction with the environment; state uncertainty, in which we do not know the current state of the environment due to imperfect perceptual information; and decision contexts involving multiple agents. The book focuses primarily on planning and reinforcement learning, although some of the techniques presented draw on elements of supervised learning and optimization. Algorithms are implemented in the Julia programming language. Figures, examples, and exercises convey the intuition behind the various approaches presented.

這本教科書廣泛介紹了在不確定性下進行決策的演算法，介紹了底層的數學問題形式和解決這些問題的演算法。

自動化決策系統或決策支援系統在應用中涵蓋了從飛機碰撞避免到乳腺癌篩檢等各種不確定性來源，必須設計成能夠平衡多個目標的系統。這本教科書廣泛介紹了在不確定性下進行決策的演算法，包括底層的數學問題形式和解決這些問題的演算法。

該書首先討論了在單一時間點上對不確定性和目標進行推理的問題，然後轉向在結果不確定的隨機環境中進行序列決策的問題。接著，它討論了模型不確定性，即我們沒有已知模型並且必須通過與環境的互動來學習如何行動；狀態不確定性，即由於感知信息不完美，我們不知道環境的當前狀態；以及涉及多個代理的決策情境。該書主要關注規劃和強化學習，儘管其中一些技術涉及監督學習和優化的元素。演算法是用Julia編程語言實現的。圖片、例子和練習題傳達了所介紹的各種方法的直觀理解。

Mykel Kochenderfer is Associate Professor at Stanford University, where he is Director of the Stanford Intelligent Systems Laboratory (SISL). He is the author of Decision Making Under Uncertainty (MIT Press). Tim Wheeler is a software engineer in the Bay Area, working on autonomy, controls, and decision-making systems. Kochenderfer and Wheeler are coauthors of Algorithms for Optimization (MIT Press). Kyle Wray is a researcher who designs and implements the decision-making systems on real-world robots.

Mykel Kochenderfer 是斯坦福大學的副教授，也是斯坦福智能系統實驗室（SISL）的主任。他是《Decision Making Under Uncertainty》（MIT Press）一書的作者。Tim Wheeler 是灣區的軟體工程師，專注於自主性、控制和決策系統。Kochenderfer 和 Wheeler 是《Algorithms for Optimization》（MIT Press）一書的合著者。Kyle Wray 是一位研究員，負責設計和實施實際機器人的決策系統。