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.
Features
Nontechnical learning material introduces major concepts using intuitive explanations, before going into mathematical or algorithmic details
A unified approach to AI shows students how the various subfields of AI fit together to build actual, useful programs.
In-depth coverage of both basic and advanced topics provides students with a basic understanding of the frontiers of AI without compromising complexity and depth.

Table of Contents
1 Introduction 2 Intelligent Agents 3 Solving Problems by Searching 4 Search in Complex Environments 5 Constraint Satisfaction Problems 6 Adversarial Search and Games 7 Logical Agents 8 First-Order Logic 9 Inference in First-Order Logic 10 Knowledge Representation 11 Automated Planning 12 Quantifying Uncertainty 13 Probabilistic Reasoning 14 Probabilistic Reasoning over Time 15 Making Simple Decisions 16 Making Complex Decisions 17 Multiagent Decision Making 18 Learning from Examples 19 Knowledge in Learning 20 Learning Probabilistic Models 21 Deep Learning 22 Reinforcement Learning 23 Natural Language Processing 24 Deep Learning for Natural Language Processing 25 Robotics 26 Computer Vision 27 Philosophy and Ethics of AI 28 Future of AI 29 Probabilistic Programming (Online)