| Descript |
1 online resource (1167 pages) |
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text txt |
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computer c |
| Carrier |
online resource cr |
| Contents |
Cover -- Half Title -- AI Pearson Series in Artificial Intelligence -- Title Page -- Copyright -- Dedication -- Preface -- About the Authors -- Contents -- I: Artificial Intelligence -- Chapter 1: Introduction -- 1.1 What Is AI? -- 1.2 The Foundations of Artificial Intelligence -- 1.3 The History of Artificial Intelligence -- 1.4 The State of the Art -- 1.5 Risks and Benefits of AI -- Summary -- Bibliographical and Historical Notes -- Chapter 2: Intelligent Agents -- 2.1 Agents and Environments -- 2.2 Good Behavior: The Concept of Rationality -- 2.3 The Nature of Environments -- 2.4 The Structure of Agents -- Summary -- Bibliographical and Historical Notes -- II: Problem-solving -- Chapter 3: Solving Problems by Searching -- 3.1 Problem-Solving Agents -- 3.2 Example Problems -- 3.3 Search Algorithms -- 3.4 Uninformed Search Strategies -- 3.5 Informed (Heuristic) Search Strategies -- 3.6 Heuristic Functions -- Summary -- Bibliographical and Historical Notes -- Chapter 4: Search in Complex Environments -- 4.1 Local Search and Optimization Problems -- 4.2 Local Search in Continuous Spaces -- 4.3 Search with Nondeterministic Actions -- 4.4 Search in Partially Observable Environments -- 4.5 Online Search Agents and Unknown Environments -- Summary -- Bibliographical and Historical Notes -- Chapter 5: Constraint Satisfaction Problems -- 5.1 Defining Constraint Satisfaction Problems -- 5.2 Constraint Propagation: Inference in CSPs -- 5.3 Backtracking Search for CSPs -- 5.4 Local Search for CSPs -- 5.5 The Structure of Problems -- Summary -- Bibliographical and Historical Notes -- Chapter 6: Adversarial Search and Games -- 6.1 Game Theory -- 6.2 Optimal Decisions in Games -- 6.3 Heuristic Alpha-Beta Tree Search -- 6.4 Monte Carlo Tree Search -- 6.5 Stochastic Games -- 6.6 Partially Observable Games -- 6.7 Limitations of Game Search Algorithms -- Summary. |
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Bibliographical and Historical Notes -- III: Knowledge, reasoning, and planning -- Chapter 7: Logical Agents -- 7.1 Knowledge-Based Agents -- 7.2 The Wumpus World -- 7.3 Logic -- 7.4 Propositional Logic: A Very Simple Logic -- 7.5 Propositional Theorem Proving -- 7.6 Effective Propositional Model Checking -- 7.7 Agents Based on Propositional Logic -- Summary -- Bibliographical and Historical Notes -- Chapter 8: First-Order Logic -- 8.1 Representation Revisited -- 8.2 Syntax and Semantics of First-Order Logic -- 8.3 Using First-Order Logic -- 8.4 Knowledge Engineering in First-Order Logic -- Summary -- Bibliographical and Historical Notes -- Chapter 9: Inference in First-Order Logic -- 9.1 Propositional vs. First-Order Inference -- 9.2 Unification and First-Order Inference -- 9.3 Forward Chaining -- 9.4 Backward Chaining -- 9.5 Resolution -- Summary -- Bibliographical and Historical Notes -- Chapter 10: Knowledge Representation -- 10.1 Ontological Engineering -- 10.2 Categories and Objects -- 10.3 Events -- 10.4 Mental Objects and Modal Logic -- 10.5 Reasoning Systems for Categories -- 10.6 Reasoning with Default Information -- Summary -- Bibliographical and Historical Notes -- Chapter 11: Automated Planning -- 11.1 Definition of Classical Planning -- 11.2 Algorithms for Classical Planning -- 11.3 Heuristics for Planning -- 11.4 Hierarchical Planning -- 11.5 Planning and Acting in Nondeterministic Domains -- 11.6 Time, Schedules, and Resources -- 11.7 Analysis of Planning Approaches -- Summary -- Bibliographical and Historical Notes -- IV: Uncertain knowledge and reasoning -- Chapter 12: Quantifying Uncertainty -- 12.1 Acting under Uncertainty -- 12.2 Basic Probability Notation -- 12.3 Inference Using Full Joint Distributions -- 12.4 Independence -- 12.5 Bayes' Rule and Its Use -- 12.6 Naive Bayes Models -- 12.7 The Wumpus World Revisited -- Summary. |
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Bibliographical and Historical Notes -- Chapter 13: Probabilistic Reasoning -- 13.1 Representing Knowledge in an Uncertain Domain -- 13.2 The Semantics of Bayesian Networks -- 13.3 Exact Inference in Bayesian Networks -- 13.4 Approximate Inference for Bayesian Networks -- 13.5 Causal Networks -- Summary -- Bibliographical and Historical Notes -- Chapter 14: Probabilistic Reasoning over Time -- 14.1 Time and Uncertainty -- 14.2 Inference in Temporal Models -- 14.3 Hidden Markov Models -- 14.4 Kalman Filters -- 14.5 Dynamic Bayesian Networks -- Summary -- Bibliographical and Historical Notes -- Chapter 15: Making Simple Decisions -- 15.1 Combining Beliefs and Desires under Uncertainty -- 15.2 The Basis of Utility Theory -- 15.3 Utility Functions -- 15.4 Multiattribute Utility Functions -- 15.5 Decision Networks -- 15.6 The Value of Information -- 15.7 Unknown Preferences -- Summary -- Bibliographical and Historical Notes -- Chapter 16: Making Complex Decisions -- 16.1 Sequential Decision Problems -- 16.2 Algorithms for MDPs -- 16.3 Bandit Problems -- 16.4 Partially Observable MDPs -- 16.5 Algorithms for Solving POMDPs -- Summary -- Bibliographical and Historical Notes -- Chapter 17: Multiagent Decision Making -- 17.1 Properties of Multiagent Environments -- 17.2 Non-Cooperative Game Theory -- 17.3 Cooperative Game Theory -- 17.4 Making Collective Decisions -- Summary -- Bibliographical and Historical Notes -- Chapter 18: Probabilistic Programming -- 18.1 Relational Probability Models -- 18.2 Open-Universe Probability Models -- 18.3 Keeping Track of a Complex World -- 18.4 Programs as Probability Models -- Summary -- Bibliographical and Historical Notes -- V: Machine Learning -- Chapter 19: Learning from Examples -- 19.1 Forms of Learning -- 19.2 Supervised Learning -- 19.3 Learning Decision Trees -- 19.4 Model Selection and Optimization. |
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19.5 The Theory of Learning -- 19.6 Linear Regression and Classification -- 19.7 Nonparametric Models -- 19.8 Ensemble Learning -- 19.9 Developing Machine Learning Systems -- Summary -- Bibliographical and Historical Notes -- Chapter 20: Knowledge in Learning -- 20.1 A Logical Formulation of Learning -- 20.2 Knowledge in Learning -- 20.3 Explanation-Based Learning -- 20.4 Learning Using Relevance Information -- 20.5 Inductive Logic Programming -- Summary -- Bibliographical and Historical Notes -- Chapter 21: Learning Probabilistic Models -- 21.1 Statistical Learning -- 21.2 Learning with Complete Data -- 21.3 Learning with Hidden Variables: The EM Algorithm -- Summary -- Bibliographical and Historical Notes -- Chapter 22: Deep Learning -- 22.1 Simple Feedforward Networks -- 22.2 Computation Graphs for Deep Learning -- 22.3 Convolutional Networks -- 22.4 Learning Algorithms -- 22.5 Generalization -- 22.6 Recurrent Neural Networks -- 22.7 Unsupervised Learning and Transfer Learning -- 22.8 Applications -- Summary -- Bibliographical and Historical Notes -- Chapter 23: Reinforcement Learning -- 23.1 Learning from Rewards -- 23.2 Passive Reinforcement Learning -- 23.3 Active Reinforcement Learning -- 23.4 Generalization in Reinforcement Learning -- 23.5 Policy Search -- 23.6 Apprenticeship and Inverse Reinforcement Learning -- 23.7 Applications of Reinforcement Learning -- Summary -- Bibliographical and Historical Notes -- VI: Communicating, perceiving, and acting -- Chapter 24: Natural Language Processing -- 24.1 Language Models -- 24.2 Grammar -- 24.3 Parsing -- 24.4 Augmented Grammars -- 24.5 Complications of Real Natural Language -- 24.6 Natural Language Tasks -- Summary -- Bibliographical and Historical Notes -- Chapter 25: Deep Learning for Natural Language Processing -- 25.1 Word Embeddings -- 25.2 Recurrent Neural Networks for NLP. |
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25.3 Sequence-to-Sequence Models -- 25.4 The Transformer Architecture -- 25.5 Pretraining and Transfer Learning -- 25.6 State of the art -- Summary -- Bibliographical and Historical Notes -- Chapter 26: Robotics -- 26.1 Robots -- 26.2 Robot Hardware -- 26.3 What kind of problem is robotics solving? -- 26.4 Robotic Perception -- 26.5 Planning and Control -- 26.6 Planning Uncertain Movements -- 26.7 Reinforcement Learning in Robotics -- 26.8 Humans and Robots -- 26.9 Alternative Robotic Frameworks -- 26.10 Application Domains -- Summary -- Bibliographical and Historical Notes -- Chapter 27: Computer Vision -- 27.1 Introduction -- 27.2 Image Formation -- 27.3 Simple Image Features -- 27.4 Classifying Images -- 27.5 Detecting Objects -- 27.6 The 3D World -- 27.7 Using Computer Vision -- Summary -- Bibliographical and Historical Notes -- VII: Conclusions -- Chapter 28: Philosophy, Ethics, and Safety of AI -- 28.1 The Limits of AI -- 28.2 Can Machines Really Think? -- 28.3 The Ethics of AI -- Summary -- Bibliographical and Historical Notes -- Chapter 29: The Future of AI -- 29.1 AI Components -- 29.2 AI Architectures -- Appendixes -- Appendix A: Mathematical Background -- A.1 Complexity Analysis and O() Notation -- A.2 Vectors, Matrices, and Linear Algebra -- A.3 Probability Distributions -- Bibliographical and Historical Notes -- Appendix B: Notes on Languages and Algorithms -- B.1 Defining Languages with Backus-Naur Form (BNF) -- B.2 Describing Algorithms with Pseudocode -- B.3 Online Supplemental Material -- Bibliography -- Index -- Symbols -- A -- B -- C -- D -- E -- F -- G -- H -- I -- J -- K -- L -- M -- N -- O -- P -- Q -- R -- S -- T -- U -- V -- W -- X -- Y -- Z. |
| Alt author |
Norvig, Peter.
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| ISBN |
9781292401171 (electronic bk.) |
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9781292401133 |
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