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• 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
• 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
• 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
• 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
• 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
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