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The logic of adaptive behavior [electronic resource] : knowledge representation and algorithms for adaptive sequential decision making under uncertainty in first-order and relational domains / Martijn van Otterlo.

By: Otterlo, Martijn van.
Contributor(s): IOS Press.
Material type: TextTextSeries: Frontiers in artificial intelligence and applications: v. 192.Publisher: Amsterdam ; Washington, D.C. : Ios Press, c2009Description: 1 online resource (xvi, 489 p.) : ill.ISBN: 9781607504061; 1607504065; 9781441616869 (electronic bk.); 1441616861 (electronic bk.).Subject(s): Decision making -- Mathematical models | Sequential analysis | Adaptive control systems | Uncertainty (Information theory) | Computer algorithms | COMPUTERS -- Enterprise Applications -- Business Intelligence Tools | COMPUTERS -- Intelligence (AI) & SemanticsGenre/Form: Electronic books. | Electronic books.Additional physical formats: Print version:: Logic of adaptive behavior.DDC classification: 006.3 Online resources: EBSCOhost
Contents:
Title page; Preface; Contents; CHAPTER 1. Introduction; 1.1. Science and Engineering of Adaptive Behavior; 1.2. You Can Only Learn What You Can Represent; 1.3. About the Contents and Structure of this Book; PART I. Learning Sequential Decision Making under Uncertainty; CHAPTER 2. Markov Decision Processes: Concepts and Algorithms; CHAPTER 3. Generalization and Abstraction in Markov Decision Processes; PART II. Sequential Decisions in the First-Order Setting; CHAPTER 4. Reasoning, Learning and Acting in Worlds with Objects; CHAPTER 5. Model-Free Algorithms for Relational MDPs.
Summary: Markov decision processes have become the de facto standard in modeling and solving sequential decision making problems under uncertainty. This book studies lifting Markov decision processes, reinforcement learning and dynamic programming to the first-order (or, relational) setting.
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Includes bibliographical references (p. [443]-476) and index.

Title page; Preface; Contents; CHAPTER 1. Introduction; 1.1. Science and Engineering of Adaptive Behavior; 1.2. You Can Only Learn What You Can Represent; 1.3. About the Contents and Structure of this Book; PART I. Learning Sequential Decision Making under Uncertainty; CHAPTER 2. Markov Decision Processes: Concepts and Algorithms; CHAPTER 3. Generalization and Abstraction in Markov Decision Processes; PART II. Sequential Decisions in the First-Order Setting; CHAPTER 4. Reasoning, Learning and Acting in Worlds with Objects; CHAPTER 5. Model-Free Algorithms for Relational MDPs.

Markov decision processes have become the de facto standard in modeling and solving sequential decision making problems under uncertainty. This book studies lifting Markov decision processes, reinforcement learning and dynamic programming to the first-order (or, relational) setting.

Description based on print version record.

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