Artificial Intelligence A Modern Approach (Global Edition)

Artificial Intelligence A Modern ApproachFree download Artificial Intelligence A Modern Approach (Global Edition) Book in PDF written by Stuart J. Russell & Peter Norvig and Published by Pearson Education Limited.

According to the Authors, “Artificial Intelligence (AI) is a big field, and this is a big book. We have tried to explore the full breadth of the field, which encompasses logic, probability, and continuous mathematics; perception, reasoning, learning, and action; fairness, trust, social good, and safety; and applications that range from microelectronic devices to robotic planetary explorers to online services with billions of users. The subtitle of this book is “A Modern Approach.” That means we have chosen to tell the story from a current perspective. We synthesize what is now known into a common framework, recasting early work using the ideas and terminology that are prevalent today. We apologize to those whose subfields are, as a result, less recognizable.

The main unifying theme is the idea of an intelligent agent. We define AI as the study of agents that receive percepts from the environment and perform actions. Each such agent implements a function that maps percept sequences to actions, and we cover different ways to represent these functions, such as reactive agents, real-time planners, decision-theoretic systems, and deep learning systems. We emphasize learning both as a construction method for competent systems and as a way of extending the reach of the designer into unknown environments. We treat robotics and vision not as independently defined problems, but as occurring in the service of achieving goals. We stress the importance of the task environment in determining the appropriate agent design.

Table of Contents

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

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