Aritificial Intelligence: A Modern Approach

Stuart J. Russell and Peter Norvig

Table of Contents
  • Part Ⅰ Artificial Intelligence
    1. 1. Introduction
    2. 2. Intelligent Agent
  • Part Ⅱ Problem-solving
    1. 3. Solving Problems By Searching
    2. 4. Beyond Classical Search
    3. 5. Adversarial Search
    4. 6. Constraint Satisfaction Problems
  • Part Ⅲ Knowledge, reasoning, and planning
    1. 7. Logical Agents
    2. 8. First Order Logic
    3. 9. Inference In First Order Logic
    4. 10. Classical Planning
    5. 11. Planning And Acting In The Real World
    6. 12. Knowledge Representation
  • Part Ⅳ Uncertain knowledge and reasoning
    1. 13. Quantifying Uncertainity
    2. 14. Probabilistic Reasoning
    3. 15. Probabilistic Reasoning Over Time
    4. 16. Making Simple Decisions
    5. 17. Making Complex Decision
  • Part Ⅴ Learning
    1. 18. Learning From Examples
    2. 19. Knowledge In Learning
    3. 20. Learning Probabilistic Models
    4. 21. Reinforcement Learning
  • Part Ⅵ Communicating, perceiving, and acting
    1. 22. Natural Language Processing
    2. 23. Natural Language For Communication
    3. 24. Perception
    4. 25. Robotics
  • Part Ⅶ Conclusions
    1. 26. Philosophical Foundations
    2. Future Exercises

AIMA-exercises is an open-source community of students, instructors and developers. Anyone can add an exercise, suggest answers to existing questions, or simply help us improve the platform. We accept contributions on this github repository.
  • Part Ⅰ Artificial Intelligence
    1. 1. Introduction
    2. 2. Intelligent Agent
  • Part Ⅱ Problem-solving
    1. 3. Solving Problems By Searching
    2. 4. Beyond Classical Search
    3. 5. Adversarial Search
    4. 6. Constraint Satisfaction Problems
  • Part Ⅲ Knowledge, reasoning, and planning
    1. 7. Logical Agents
    2. 8. First Order Logic
    3. 9. Inference In First Order Logic
    4. 10. Classical Planning
    5. 11. Planning And Acting In The Real World
    6. 12. Knowledge Representation
  • Part Ⅳ Uncertain knowledge and reasoning
    1. 13. Quantifying Uncertainity
    2. 14. Probabilistic Reasoning
    3. 15. Probabilistic Reasoning Over Time
    4. 16. Making Simple Decisions
    5. 17. Making Complex Decision
  • Part Ⅴ Learning
    1. 18. Learning From Examples
    2. 19. Knowledge In Learning
    3. 20. Learning Probabilistic Models
    4. 21. Reinforcement Learning
  • Part Ⅵ Communicating, perceiving, and acting
    1. 22. Natural Language Processing
    2. 23. Natural Language For Communication
    3. 24. Perception
    4. 25. Robotics
  • Part Ⅶ Conclusions
    1. 26. Philosophical Foundations
    2. Future Exercises