Overview

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

Full Product Details

Author:   Mykel J. Kochenderfer ,  Tim A. Wheeler ,  Kyle H. Wray
Publisher:   MIT Press Ltd
Imprint:   MIT Press
Weight:   0.567kg
ISBN:  

9780262047012

ISBN 10:   0262047012
Pages:   704
Publication Date:   16 August 2022
Audience:   General/trade ,  General
Format:   Hardback
Publisher's Status:   Active
Availability:   To order  
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Table of Contents

Preface xix Acknowledgments xxi 1 Introduction 1 Part I Probabilistic Reasoning 2 Representation 19 3 Inference 43 4 Parameter Learning 71 5 Structure Learning 97 6 Simple Decisions 111 Part II Sequential Problems 7 Exact Solution Methods 133 8 Approximate Value Functions 161 9 Online Planning 181 10 Policy Search 213 11 Policy Gradient Estimation 231 12 Policy Gradient Optimization 249 13 Actor-Critic Methods 267 14 Policy Validation 281 Part III Model Uncertainty 15 Exploration and Exploitation 299 16 Model-Based Methods 317 17 Model-Free Methods 335 18 Imitation Learning 335 Part IV State Uncertainty 19 Beliefs 379 20 Exact Belief State Planning 407 21 Offline Belief State Planning 427 22 Online Belief State Planning 453 23 Controller Abstractions 471 Part V Multiagent Systems 24 Multiagent Reasoning 493 25 Sequential Problems 517 26 State Uncertainty 533 27 Collaborative Agents 545 Appendices A Mathematical Concepts 561 B Probability Distributions 573 C Computational Complexity 575 D Neural Representations 581 E Search Algorithms 599 F Problems 609 G Julia 627 References 651 Index 671

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Author Information

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.

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