Artificial Intelligence, A Modern Approach, 4th Edition, 9781292401133

• Stuart Russell
• Peter Norvig

4th edition | Published by Pearson (May 13th 2021) - Copyright © 2021

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, present 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.

Table of Contents

Part I: ArtificialIntelligence
1. Introduction
    1.1  What Is AI?
    1.2  The Foundations of Artificial Intelligence
    1.3  The History of Artificial Intelligence
    1.4  The State of the Art
    1.5  Risks and Benefits of AI
2. Intelligent Agents
    2.1  Agents and Environments
    2.2  Good Behavior: The Concept of Rationality
    2.3  The Nature of Environments
    2.4  The Structure of Agents

Part II: Problem Solving
3. Solving Problems by Searching
    3.1  Problem-Solving Agents
    3.2  Example Problems
    3.3  Search Algorithms
    3.4  Uninformed Search Strategies
    3.5  Informed (Heuristic) Search Strategies
    3.6  Heuristic Functions
4. Search in Complex Environments
    4.1  Local Search and Optimization Problems
    4.2  Local Search in Continuous Spaces
    4.3  Search with Nondeterministic Actions
    4.4  Search in Partially Observable Environments
    4.5  Online Search Agents and Unknown Environments
5. Constraint Satisfaction Problems
    5.1  Defining Constraint Satisfaction Problems
    5.2  Constraint Propagation: Inference in CSPs
    5.3  Backtracking Search for CSPs
    5.4  Local Search for CSPs
    5.5  The Structure of Problems
6. Adversarial Search and Games
    6.1  Game Theory
    6.2  Optimal Decisions in Games
    6.3  Heuristic Alpha--Beta Tree Search
    6.4  Monte Carlo Tree Search
    6.5  Stochastic Games
    6.6  Partially Observable Games
    6.7  Limitations of Game Search Algorithms

Part III: Knowledge and Reasoning
7. Logical Agents
    7.1  Knowledge-Based Agents
    7.2  The Wumpus World
    7.3  Logic
    7.4  Propositional Logic: A Very Simple Logic
    7.5  Propositional Theorem Proving
    7.6  Effective Propositional Model Checking
    7.7  Agents Based on Propositional Logic
8. First-Order Logic
    8.1  Representation Revisited
    8.2  Syntax and Semantics of First-Order Logic
    8.3  Using First-Order Logic
    8.4  Knowledge Engineering in First-Order Logic
9. Inference in First-Order Logic
    9.1  Propositional vs.~First-Order Inference
    9.2  Unification and First-Order Inference
    9.3  Forward Chaining
    9.4  Backward Chaining
    9.5  Resolution
10. Knowledge Representation
    10.1  Ontological Engineering
    10.2  Categories and Objects
    10.3  Events
    10.4  Mental Objects and Modal Logic
    10.5  Reasoning Systems for Categories
    10.6  Reasoning with Default Information
11. Automated Planning
    11.1  Definition of Classical Planning
    11.2  Algorithms for Classical Planning
    11.3  Heuristics for Planning
    11.4  Hierarchical Planning
    11.5  Planning and Acting in Nondeterministic Domains
    11.6  Time, Schedules, and Resources
    11.7  Analysis of Planning Approaches
12. Quantifying Uncertainty
    12.1  Acting under Uncertainty
    12.2  Basic Probability Notation
    12.3  Inference Using Full Joint Distributions
    12.4  Independence
    12.5  Bayes' Rule and Its Use
    12.6  Naive Bayes Models
    12.7  The Wumpus World Revisited

Part IV: Uncertain Knowledge and Reasoning
13. Probabilistic Reasoning
    13.1  Representing Knowledge in an Uncertain Domain
    13.2  The Semantics of Bayesian Networks
    13.3  Exact Inference in Bayesian Networks
    13.4  Approximate Inference for Bayesian Networks
    13.5  Causal Networks
14. Probabilistic Reasoning over Time
    14.1  Time and Uncertainty
    14.2  Inference in Temporal Models
    14.3  Hidden Markov Models
    14.4  Kalman Filters
    14.5  Dynamic Bayesian Networks
15. Making Simple Decisions
    16.1  Combining Beliefs and Desires under Uncertainty
    16.2  The Basis of Utility Theory
    16.3  Utility Functions
    16.4  Multiattribute Utility Functions
    16.5  Decision Networks
    16.6  The Value of Information
    16.7  Unknown Preferences
16. Making Complex Decisions
    17.1  Sequential Decision Problems
    17.2  Algorithms for MDPs
    17.3  Bandit Problems
    17.4  Partially Observable MDPs
    17.5  Algorithms for solving POMDPs

Part V: Learning
17. Multiagent Decision Making
    17.1  Properties of Multiagent Environments
    17.2  Non-Cooperative Game Theory
    17.3  Cooperative Game Theory
    17.4  Making Collective Decisions

18. ProbabilisticProgramming
    18.1  Relational Probability Models
    18.2  Open-Universe Probability Models
    18.3  Keeping Track of a Complex World
    18.4  Programs as Probability Models

19. Learning fromExamples
    19.1  Forms of Learning
    19.2  Supervised Learning
    19.3  Learning Decision Trees
    19.4  Model Selection and Optimization
    19.5  The Theory of Learning
    19.6  Linear Regression and Classification
    19.7  Nonparametric Models
    19.8  Ensemble Learning
    19.9  Developing Machine Learning Systems

20. Knowledge inLearning

20.1 A Logical Formulation of Learning

20.2 Knowledge in Learning

20.3 Explanation-Based Learning

20.4 Learning Using Relevance Information

20.5 Inductive Logic Programming

21. LearningProbabilistic Models
    21.1  Statistical Learning
    21.2  Learning with Complete Data
    21.3  Learning with Hidden Variables: The EM Algorithm
22. Deep Learning
    22.1  Simple Feedforward Networks
    22.2  Mixing and matching models, loss functions andoptimizers
    22.3  Loss functions
    22.4  Models
    22.5  Optimization Algorithms
    22.6  Generalization
    22.7  Recurrent neural networks
    22.8  Unsupervised, semi-supervised and transferlearning
    22.9  Applications

Part VI: Communicating, Perceiving, and Acting
23. Reinforcement Learning
    23.1  Learning from Rewards
    23.2  Passive Reinforcement Learning
    23.3  Active Reinforcement Learning
    23.4  Safe Exploration
    23.5  Generalization in Reinforcement Learning
    23.6  Policy Search
    23.7  Applications of Reinforcement Learning
24. Natural Language Processing
    24.1  Language Models
    24.2  Grammar
    24.3  Parsing
    24.4  Augmented Grammars
    24.5  Complications of Real Natural Language
    24.6  Natural Language Tasks
25. Deep Learning for Natural Language Processing
    25.1  Limitations of Feature-Based NLP Models
    25.2  Word Embeddings
    25.3  Recurrent Neural Networks
    25.4  Sequence-to-sequence Models
    25.5  The Transformer Architecture
    25.6  Pretraining and Transfer Learning

26. Robotics
    26.1  Robots
    26.2  Robot Hardware
    26.3  What kind of problem is robotics solving?
    26.4  Robotic Perception
    26.5  Planning and Control
    26.6  Planning Uncertain Movements
    26.7  Reinforcement Learning in Robotics
    26.8  Humans and Robots
    26.9  Alternative Robotic Frameworks
    26.10 Application Domains
27. Computer Vision

    27.1 Introduction
    27.2 Image Formation
    27.3  Simple Image Features
    27.4 Classifying Images
    27.5 Detecting Objects
    27.6 The 3D World
    27.7 Using Computer Vision

Part VII: Conclusions
28. Philosophy and Ethics of AI
    28.1  Weak AI: What are the Limits of AI?
    28.2  Strong AI: Can Machines Really Think?
    28.3  The Ethics of AI
29. The Future of AI
    29.1  AI Components
    29.2  AI Architectures

Appendix A:Mathematical Background
    A.1  Complexity Analysis and O() Notation
    A.2  Vectors, Matrices, and Linear Algebra
    A.3  Probability Distributions
Appendix B: Notes on Languages and Algorithms
    B.1  Defining Languages with Backus--Naur Form (BNF)
    B.2  Describing Algorithms with Pseudocode
    B.3  Online Supplemental Material