Artificial Intelligence: A Modern Approach, Global Edition

Thelong-anticipated revision of ArtificialIntelligence: A Modern Approach explores the full breadth and depth of the field of artificialintelligence (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 coverageof machine learning, deep learning, transfer learning, multi agent systems,robotics, natural language processing, causality, probabilistic programming,privacy, fairness, and safe AI.

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
   SummaryBibliographical and Historical Notes
2. Intelligent Agents
   • Agents and Environments
   • Good Behavior: The Concept of Rationality
   • The Nature of Environments
   • The Structure of Agents
   SummaryBibliographical and Historical Notes
3. Chapter II  Problem Solving
4. Solving Problems by Searching
   • Problem-Solving Agents
   • Example Problems
   • Search Algorithms
   • Uninformed Search Strategies
   • Informed (Heuristic) Search Strategies
   • Heuristic Functions
   SummaryBibliographical and Historical Notes
5. 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
   SummaryBibliographical and Historical Notes
6. Constraint Satisfaction Problems
   • Defining Constraint Satisfaction Problems
   • Constraint Propagation: Inference in CSPs
   • Backtracking Search for CSPs
   • Local Search for CSPs
   • The Structure of Problems
   SummaryBibliographical and Historical Notes
7. 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
   SummaryBibliographical and Historical Notes
8. Chapter III  Knowledge, Reasoning and Planning
9. 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
   SummaryBibliographical and Historical Notes
10. First-Order Logic
    • Representation Revisited
    • Syntax and Semantics of First-Order Logic
    • Using First-Order Logic
    • Knowledge Engineering in First-Order Logic
    SummaryBibliographical and Historical Notes
11. Inference in First-Order Logic
    • Propositional vs. First-Order Inference
    • Unification and First-Order Inference
    • Forward Chaining
    • Backward Chaining
    • Resolution
    SummaryBibliographical and Historical Notes
12. Knowledge Representation
    • Ontological Engineering
    • Categories and Objects
    • Events
    • Mental Objects and Modal Logic
    • for Categories
    • Reasoning with Default Information
    SummaryBibliographical and Historical Notes
13. Automated Planning
    • Definition of Classical Planning
    • Algorithms for Classical Planning
    • Heuristics for Planning
    • Hiera