Artificial Intelligence: A Modern Approach, 4th edition

  • Stuart Russell
  • Peter Norvig


The most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence
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, presents concepts in a more unified manner, and offers new or expanded coverage of machine learning, deep learning, transfer learning, multiagent systems, robotics, natural language processing, causality, probabilistic programming, privacy, fairness, and safe AI.

Table of contents

Part I: Artificial Intelligence
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. Adversarial Search and Games
    5.1  Game Theory
    5.2  Optimal Decisions in Games
    5.3  Heuristic Alpha--Beta Tree Search
    5.4  Monte Carlo Tree Search
    5.5  Stochastic Games
    5.6  Partially Observable Games
    5.7  Limitations of Game Search Algorithms
6. Constraint Satisfaction Problems
    6.1  Defining Constraint Satisfaction Problems
    6.2  Constraint Propagation: Inference in CSPs
    6.3  Backtracking Search for CSPs
    6.4  Local Search for CSPs
    6.5  The Structure of Problems
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. Probabilistic Programming
    15.1  Relational Probability Models
    15.2  Open-Universe Probability Models
    15.3  Keeping Track of a Complex World
    15.4  Programs as Probability Models
16. 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
17. 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
18. Multiagent Decision Making
    18.1  Properties of Multiagent Environments
    18.2  Non-Cooperative Game Theory
    18.3  Cooperative Game Theory
    18.4  Making Collective Decisions
19. Learning from Examples
    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. Learning Probabilistic Models
    20.1  Statistical Learning
    20.2  Learning with Complete Data
    20.3  Learning with Hidden Variables: The EM Algorithm
21. Deep Learning
    21.1  Simple Feedforward Networks
    21.2  Mixing and matching models, loss functions and optimizers
    21.3  Loss functions
    21.4  Models
    21.5  Optimization Algorithms
    21.6  Generalization
    21.7  Recurrent neural networks
    21.8  Unsupervised, semi-supervised and transfer learning
    21.9  Applications
Part VI: Communicating, Perceiving, and Acting
22. Reinforcement Learning
    22.1  Learning from Rewards
    22.2  Passive Reinforcement Learning
    22.3  Active Reinforcement Learning
    22.4  Safe Exploration
    22.5  Generalization in Reinforcement Learning
    22.6  Policy Search
    22.7  Applications of Reinforcement Learning
23. Natural Language Processing
    23.1  Language Models
    23.2  Grammar
    23.3  Parsing
    23.4  Augmented Grammars
    23.5  Complications of Real Natural Language
    23.6  Natural Language Tasks
24. Deep Learning for Natural Language Processing
    24.1  Limitations of Feature-Based NLP Models
    24.2  Word Embeddings
    24.3  Recurrent Neural Networks
    24.4  Sequence-to-sequence Models
    24.5  The Transformer Architecture
    24.6  Pretraining and Transfer Learning
    24.7  Introduction
    24.8  Image Formation
    24.9  Simple Image Features
    24.10 Classifying Images
    24.11 Detecting Objects
    24.12 The 3D World
    24.13 Using Computer Vision
25. Robotics
    25.1  Robots
    25.2  Robot Hardware
    25.3  What kind of problem is robotics solving?
    25.4  Robotic Perception
    25.5  Planning and Control
    25.6  Planning Uncertain Movements
    25.7  Reinforcement Learning in Robotics
    25.8  Humans and Robots
    25.9  Alternative Robotic Frameworks
    25.10 Application Domains
Part VII: Conclusions
26. Philosophy and Ethics of AI
    26.1  Weak AI: What are the Limits of AI?
    26.2  Strong AI: Can Machines Really Think?
    26.3  The Ethics of AI
27. The Future of AI
    27.1  AI Components
    27.2  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


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Published by Pearson (April 28th 2020) - Copyright © 2020