Artificial Intelligence: A Modern Approach, Global Edition

Höfundar: Stuart Russell; Peter Norvig (Útgáfa: 4)
Artificial Intelligence: A Modern Approach, Global Edition

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The long-anticipated revision of Artificial Intelligence: A Modern Approach explores the full breadth and depth of the field of artificial intelligence (AI). The 4th Edition brings readers up to date on the latest technologies, present concepts in a more unified manner, and offers new or expanded coverage of machine learning, deep learning, transfer learning, multi agent systems, robotics, natural language processing, causality, probabilistic programming, privacy, fairness, and safe AI.

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Útgefandi
Pearson International Content
ISBN
9781292410074
Print ISBN
9781292401133
Format
ePub
Útgáfa
4
Höfundar
Stuart Russell; Peter Norvig
Tungumál
English
Útgefið
2021-12-21
Prent takmörkun á líftíma
100
Prent takmörkun
2
Afritunar takmörkun
2

Kaflar

  • Cover Page
  • Title
  • Copyright
  • Preface
  • About the Authors
  • Contents
  • I Artificial Intelligence
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 29 The Future of AI
  • 29.1 AI Components
  • 29.2 AI Architectures
  • 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
  • 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