Artificial Intelligence: A Modern Approach, Global Edition, 4th edition

Published by Pearson (December 20, 2021) © 2022

  • Stuart Russell University of California at Berkeley
  • Peter Norvig
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A comprehensive and accessible introduction to the theory and practice of AI

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, presents 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.

  • Chapter 1: Introduction
  • Chapter 2: Intelligent Agents
  • Chapter 3: Solving Problems by Searching
  • Chapter 4: Search in Complex Environments
  • Chapter 5: Constraint Satisfaction Problems
  • Chapter 6: Adversarial Search and Games
  • Chapter 7: Logical Agents
  • Chapter 8: First-Order Logic
  • Chapter 9: Inference in First-Order Logic
  • Chapter 10: Knowledge Representation
  • Chapter 11: Automated Planning
  • Chapter 12: Quantifying Uncertainty
  • Chapter 13: Probabilistic Reasoning
  • Chapter 14: Probabilistic Reasoning over Time
  • Chapter 15: Making Simple Decisions
  • Chapter 16: Making Complex Decisions
  • Chapter 17: Multiagent Decision Making
  • Chapter 18: Probabilistic Programming
  • Chapter 19: Learning from Examples
  • Chapter 20: Knowledge in Learning
  • Chapter 21: Learning Probabilistic Models
  • Chapter 22: Deep Learning
  • Chapter 23: Reinforcement Learning
  • Chapter 24: Natural Language Processing
  • Chapter 25: Deep Learning for Natural Language Processing
  • Chapter 26: Robotics
  • Chapter 27: Computer Vision
  • Chapter 28: Philosophy, Ethics, and Safety of AI
  • Chapter 29: The Future of AI

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