Artificial Intelligence: A Modern Approach by Stuart Russell - Book Review


Several new AI textbooks have been released over the last three years, all written mostly by scholars. Therefore, it is not shocking that the authors of all of these textbooks have found a way to differentiate their book from the others. Throughout the book, attention is paid to how specific AI algorithms can be integrated into a larger entity that interacts with its environment.

Which made this textbook so unique? First, it is indeed surprisingly detailed. Its eight key sections cover almost all the AI student wants to know to begin learning primary literature. Each section consists of many chapters that provide comprehensive coverage of the relevant content. Not only does the book include ample context to start significant research in AI, but it also contains just the essential context.

What Will You Learn?

  • Set the tone for the upcoming chapters by viewing Artificial intelligence as smart agents that can determine what measures to take or when to take them. 
  • Focus on strategies for determining what action can be taken when you need to think a few moves ahead, such as playing the game of chess. 
  • Discuss approaches to portray knowledge about the world of artificial intelligence and reason for that knowledge theoretically.
  • Deals with logic and decision-making in the face of environmental complexity. 
  • Describes ways of producing the information required by the decision-making frameworks and brings a new component, an 'Artificial Neural Network.' 
  • View 'Artificial Intelligence' as smart agents that can decide what action to take or what to take.
  • Focus on tactics to decide what action should be taken when you're about to learn a few steps forward, such as playing chess games. 
  • Discuss ways to represent knowledge about the field of 'Artificial Intelligence' and clarify that knowledge potentially.


This book is a textbook concerning Artificial Intelligence and was published in 1995, written by Stuart Russell and Peter Norvig. The book is intended for 'undergraduate students' but can also be used by whoever wants to. In this book, there's an addition of some of the primary sources contained in the comprehensive reference list.

It seems to be the most outstanding work on the market in terms of scope and coverage depth. I like the emphasis on issues that are most likely to impact designing and evaluating present and future AI systems. I am also a big fan of ideas and strategies described precisely and whose strengths and weaknesses are reasonably well known, particularly in intro books.

The authors present this fast-moving science in a straightforward, inviting manner. The book blends sound theoretical research with realistic examples that, together, discuss and illuminate key issues. It is brimming with confidence, optimism, and infectious enthusiasm about the AI borders without losing any of the sector's scope and depth. The book will be embraced and appreciated by professors and students, respectively.

All in all, this is incredibly accessible and fascinating for a textbook. If you have any interest in the subject, this is the book you may want to read. This is popular with AI classes, so any decent college libraries must have a copy of it.

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