{"product_id":"algorithms-for-decision-making-isbn-9780262047012","title":"Algorithms for Decision Making","description":"\u003cb\u003eA broad introduction to algorithms for decision making under uncertainty, introducing the underlying mathematical problem formulations and the algorithms for solving them.\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003eAutomated 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.\u003cbr\u003e \u003cbr\u003eThe 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.Preface xix\u003cbr\u003eAcknowledgments xxi\u003cbr\u003e1 Introduction 1\u003cbr\u003ePart I Probabilistic Reasoning\u003cbr\u003e2 Representation 19\u003cbr\u003e3 Inference 43\u003cbr\u003e4 Parameter Learning 71\u003cbr\u003e5 Structure Learning 97\u003cbr\u003e6 Simple Decisions 111\u003cbr\u003ePart II Sequential Problems\u003cbr\u003e7 Exact Solution Methods 133\u003cbr\u003e8 Approximate Value Functions 161\u003cbr\u003e9 Online Planning 181\u003cbr\u003e10 Policy Search 213\u003cbr\u003e11 Policy Gradient Estimation 231\u003cbr\u003e12 Policy Gradient Optimization 249\u003cbr\u003e13 Actor-Critic Methods 267\u003cbr\u003e14 Policy Validation 281\u003cbr\u003ePart III Model Uncertainty\u003cbr\u003e15 Exploration and Exploitation 299\u003cbr\u003e16 Model-Based Methods 317\u003cbr\u003e17 Model-Free Methods 335\u003cbr\u003e18 Imitation Learning 335\u003cbr\u003ePart IV State Uncertainty \u003cbr\u003e19 Beliefs 379\u003cbr\u003e20 Exact Belief State Planning 407\u003cbr\u003e21 Offline Belief State Planning 427\u003cbr\u003e22 Online Belief State Planning 453\u003cbr\u003e23 Controller Abstractions 471\u003cbr\u003ePart V Multiagent Systems\u003cbr\u003e24 Multiagent Reasoning 493\u003cbr\u003e25 Sequential Problems 517\u003cbr\u003e26 State Uncertainty 533\u003cbr\u003e27 Collaborative Agents 545\u003cbr\u003eAppendices \u003cbr\u003eA Mathematical Concepts 561\u003cbr\u003eB Probability Distributions 573\u003cbr\u003eC Computational Complexity 575\u003cbr\u003eD Neural Representations 581\u003cbr\u003eE Search Algorithms 599\u003cbr\u003eF Problems 609\u003cbr\u003eG Julia 627\u003cbr\u003eReferences 651\u003cbr\u003eIndex 671“Its remarkable clarity, range, and depth make this a magnificent book both to learn from and to teach. It opens the door to so many modern techniques while firmly grounding them in the statistical and mathematical theory given us by the founders. t is a wonderful book—truly exceptional.”\u003cbr\u003e \u003cb\u003e—Thomas J. Sargent, Department of Economics, New York University, Senior Fellow, Hoover Institution, Stanford University\u003c\/b\u003e\u003cbr\u003e  \u003cbr\u003e “I love the topics covered—a great mix of classical approaches and more recent trends. It'll be my main textbook for teaching reinforcement learning.”\u003cbr\u003e \u003cb\u003e—Michael L. Littman, Professor of Computer Science, Brown University \u003c\/b\u003eMykel Kochenderfer is Associate Professor at Stanford University, where he is Director of the Stanford Intelligent Systems Laboratory (SISL). He is the author of \u003ci\u003eDecision Making Under Uncertainty\u003c\/i\u003e (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 \u003ci\u003eAlgorithms for Optimization \u003c\/i\u003e(MIT Press). Kyle Wray is a researcher who designs and implements the decision-making systems on real-world robots.","brand":"The MIT Press","offers":[{"title":"Default Title","offer_id":46302654038245,"sku":"NP9780262047012","price":95.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9780262047012.jpg?v=1767721217","url":"https:\/\/k12savings.com\/products\/algorithms-for-decision-making-isbn-9780262047012","provider":"K12savings","version":"1.0","type":"link"}