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

Chapter I  Artificial Intelligence

1. Introduction
   • What Is AI?
   • The Foundations of Artificial Intelligence
   • The History of Artificial Intelligence
   • The State of the Art
   • Risks and Benefits of AI

   Summary

   Bibliographical and Historical Notes

2. Intelligent Agents
   • Agents and Environments
   • Good Behavior: The Concept of Rationality
   • The Nature of Environments
   • The Structure of Agents

   Summary

   Bibliographical and Historical Notes

Chapter II  Problem Solving

3. Solving Problems by Searching
   • Problem-Solving Agents
   • Example Problems
   • Search Algorithms
   • Uninformed Search Strategies
   • Informed (Heuristic) Search Strategies
   • Heuristic Functions

   Summary

   Bibliographical and Historical Notes

4. Search in Complex Environments
   • Local Search and Optimization Problems
   • Local Search in Continuous Spaces
   • Search with Nondeterministic Actions
   • Search in Partially Observable Environments
   • Online Search Agents and Unknown Environments

   Summary

   Bibliographical and Historical Notes

5. Constraint Satisfaction Problems
   • Defining Constraint Satisfaction Problems
   • Constraint Propagation: Inference in CSPs
   • Backtracking Search for CSPs
   • Local Search for CSPs
   • The Structure of Problems

   Summary

   Bibliographical and Historical Notes

6. Adversarial Search and Games
   • Game Theory
   • Optimal Decisions in Games
   • Heuristic Alpha--Beta Tree Search
   • Monte Carlo Tree Search
   • Stochastic Games
   • Partially Observable Games
   • Limitations of Game Search Algorithms

   Summary

   Bibliographical and Historical Notes

Chapter III  Knowledge, Reasoning and Planning

7. Logical Agents
   • Knowledge-Based Agents
   • The Wumpus World
   • Logic
   • Propositional Logic: A Very Simple Logic
   • Propositional Theorem Proving
   • Effective Propositional Model Checking
   • Agents Based on Propositional Logic

   Summary

   Bibliographical and Historical Notes

8. First-Order Logic
   • Representation Revisited
   • Syntax and Semantics of First-Order Logic
   • Using First-Order Logic
   • Knowledge Engineering in First-Order Logic

   Summary

   Bibliographical and Historical Notes

9. Inference in First-Order Logic
   • Propositional vs. First-Order Inference
   • Unification and First-Order Inference
   • Forward Chaining
   • Backward Chaining
   • Resolution

   Summary

   Bibliographical and Historical Notes

10. Knowledge Representation
    • Ontological Engineering
    • Categories and Objects
    • Events
    • Mental Objects and Modal Logic
    • for Categories
    • Reasoning with Default Information

    Summary

    Bibliographical and Historical Notes

11. Automated Planning
    • Definition of Classical Planning
    • Algorithms for Classical Planning
    • Heuristics for Planning
    • Hierarchical Planning
    • Planning and Acting in Nondeterministic Domains
    • Time, Schedules, and Resources
    • Analysis of Planning Approaches

    Summary

    Bibliographical and Historical Notes

Chapter IV  Uncertain Knowledge and Reasoning

12. Quantifying Uncertainty
    • Acting under Uncertainty
    • Basic Probability Notation
    • Inference Using Full Joint Distributions
    • Independence 12.5 Bayes' Rule and Its Use
    • Naive Bayes Models
    • The Wumpus World Revisited

    Summary

    Bibliographical and Historical Notes

13. Probabilistic Reasoning
    • Representing Knowledge in an Uncertain Domain
    • The Semantics of Bayesian Networks
    • Exact Inference in Bayesian Networks
    • Approximate Inference for Bayesian Networks
    • Causal Networks

    Summary

    Bibliographical and Historical Notes

14. Probabilistic Reasoning over Time
    • Time and Uncertainty
    • Inference in Temporal Models
    • Hidden Markov Models
    • Kalman Filters
    • Dynamic Bayesian Networks

    Summary

    Bibliographical and Historical Notes

15. Making Simple Decisions
    • Combining Beliefs and Desires under Uncertainty
    • The Basis of Utility Theory
    • Utility Functions
    • Multiattribute Utility Functions
    • Decision Networks
    • The Value of Information
    • Unknown Preferences

    Summary

    Bibliographical and Historical Notes

16. Making Complex Decisions
    • Sequential Decision Problems
    • Algorithms for MDPs
    • Bandit Problems
    • Partially Observable MDPs
    • Algorithms for Solving POMDPs

    Summary

    Bibliographical and Historical Notes

17. Multiagent Decision Making
    • Properties of Multiagent Environments
    • Non-Cooperative Game Theory
    • Cooperative Game Theory
    • Making Collective Decisions

    Summary

    Bibliographical and Historical Notes

18. Probabilistic Programming
    • Relational Probability Models
    • Open-Universe Probability Models
    • Keeping Track of a Complex World
    • Programs as Probability Models

    Summary

    Bibliographical and Historical Notes

Chapter V  Machine Learning

19. Learning from Examples
    • Forms of Leaming
    • Supervised Learning .
    • Learning Decision Trees .
    • Model Selection and Optimization
    • The Theory of Learning
    • Linear Regression and Classification
    • Nonparametric Models
    • Ensemble Learning
    • Developing Machine Learning Systen

    Summary

    Bibliographical and Historical Notes

20. Knowledge in Learning
    • A Logical Formulation of Learning
    • Knowledge in Learning
    • Exmplanation-Based Leaening
    • Learning Using Relevance Information
    • Inductive Logic Programming

    Summary

    Bibliographical and Historical Notes

21. Learning Probabilistic Models
    • Statistical Learning
    • Learning with Complete Data
    • Learning with Hidden Variables: The EM Algorithm

    Summary

    Bibliographical and Historical Notes

22. Deep Learning
    • Simple Feedforward Networks
    • Computation Graphs for Deep Learning
    • Convolutional Networks
    • Learning Algorithms
    • Generalization
    • Recurrent Neural Networks
    • Unsupervised Learning and Transfer Learning
    • Applications

    Summary

    Bibliographical and Historical Notes

23. Reinforcement Learning
    • Learning from Rewards
    • Passive Reinforcement Learning
    • Active Reinforcement Learning
    • Generalization in Reinforcement Learning
    • Policy Search
    • Apprenticeship and Inverse Reinforcement Leaming
    • Applications of Reinforcement Learning

    Summary

    Bibliographical and Historical Notes

Chapter VI  Communicating, perceiving, and acting

24. Natural Language Processing
    • Language Models
    • Grammar
    • Parsing
    • Augmented Grammars
    • Complications of Real Natural Languagr
    • Natural Language Tasks

    Summary

    Bibliographical and Historical Notes

25. Deep Learning for Natural Language Processing
    • Word Embeddings
    • Recurrent Neural Networks for NLP
    • Sequence-to-Sequence Models
    • The Transformer Architecture
    • Pretraining and Transfer Learning
    • State of the art

    Summary

    Bibliographical and Historical Notes

26. Robotics
    • Robots
    • Robot Hardware
    • What kind of problem is robotics solving?
    • Robotic Perception
    • Planning and Control
    • Planning Uncertain Movements
    • Reinforcement Laming in Robotics
    • Humans and Robots
    • Alternative Robotic Frameworks
    • Application Domains

    Summary

    Bibliographical and Historical Notes

27. Computer Vision
    • Introduction
    • Image Formation
    • Simple Image Features
    • Classifying Images
    • Detecting Objects
    • The 3D World
    • Using Computer Vision

    Summary

    Bibliographical and Historical Notes

Chapter VII  Conclusions

28. Philosophy, Ethics, and Safety of Al
    • The Limits of Al
    • Can Machines Really Think?
    • The Ethics of Al

    Summary

    Bibliographical and Historical Notes

29. The Future of AI
    • Al Components
    • Al Architectures