Artificial Intelligence: A Modern Approach

A landmark AI textbook, fully updated for modern practice

Artificial Intelligence: A Modern Approach is the kind of textbook that shapes how people understand the field. In this 4th edition, Stuart Russell and Peter Norvig present AI as the study of intelligent agents—systems that perceive, reason, and act—while organizing the subject into a clear, broad framework that runs from search and logic to probabilistic reasoning, learning, planning, and robotics.

This edition reflects how much the discipline has changed: machine learning receives more emphasis, deep learning and probabilistic programming are expanded, and the book gives serious attention to fairness, trust, safety, and the social impact of AI. The result is a work that feels current without losing the depth that has made the title so influential for years.

What the book covers

• Search, game playing, and constraint satisfaction
• Logical representation and inference
• Planning and decision-making under uncertainty
• Bayesian networks, temporal models, and probabilistic programming
• Multiagent systems and machine learning
• Robotics, vision, natural language, and broader AI applications

Why it stands out

The book is designed to connect AI topics into one coherent story instead of treating them as disconnected techniques. That makes it especially useful for readers who want more than isolated recipes: it explains how the ideas fit together, why they matter, and how modern AI systems are built and evaluated.

It is also substantial. With extensive chapter coverage, mathematical treatment where needed, pseudocode algorithms, and a course-oriented structure, it works as both a teaching text and a long-term reference.

Who it’s for

This is a strong fit for undergraduate and graduate computer science students, instructors, and technically minded readers who want a serious introduction to AI. If you want a broad, respected, and methodical overview of the field, this is one of the defining texts.