Artificial Intelligence: A Modern Approach (Pearson Series in Artifical Intelligence)

Best AI book, Artificial Intelligence: A Modern Approach (Pearson Series in Artifical Intelligence).

A promising version of artificial intelligence: A modern approach explores the  breadth and depth of the field of artificial intelligence (AI). The fourth edition introduces readers  to  the latest technologies, presents concepts in a more integrated way, and introduces new and expanded topics in machine learning, deep learning, transfer learning, multi-agent systems, robotics, natural language processing, causality, and probabilistic programming. It provides scope. , Privacy, Fairness and Security AI.

Artificial Intelligence

A Modern Approach (Pearson Series in Artifical Intelligence) 

4th Edition

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This book provides the most complete and up-to-date introduction to the theory and practice of artificial intelligence. Non-technical learning materials introduce basic concepts using intuitive explanations before moving on to mathematical or algorithmic details. Using non-technical language makes this book accessible to more readers.  A Consolidated Approach to AI shows students how the various AI subfields can be combined with each other to create practical and useful programs.

Artificial Intelligence: A Modern Approach (Pearson Series in Artifical Intelligence) 4th Edition

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Offer the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence

  • The basic definition of AI systems is generalized to eliminate the standard assumption that the objective is fixed and known by the intelligent agent; instead, the agent may be uncertain about the true objectives of the human(s) on whose behalf it operates.
  • The Author-Maintained Website at includes text-related comments and discussions, exercises, an online code repository, Instructor Resources, and more!
  • Interactive student exercises are now featured on the website to allow for continuous updating and additions.
  • Updated online software gives students more opportunities to complete projects, including implementations of the algorithms in the book, plus supplemental coding examples and applications in Python, Java, and Javascript.
  • New instructional video tutorials deepen students’ engagement and bring key concepts to life.Stay current with the latest technologies and present concepts in a more unified manner
  • New chapters feature expanded coverage of probabilistic programming (Ch. 15); multiagent decision making (Ch. 18 with Michael Wooldridge); deep learning (Ch. 21 with Ian Goodfellow); and deep learning for natural language processing (Ch. 24 with Jacob Devlin and Mei-Wing Chang).
  • Increased coverage of machine learning.
  • Significantly updated material on robotics includes robots that interact with humans and the application of reinforcement learning to robotics.
  • New section on causality by Judea Pearl.
  • New sections on Monte Carlo search for games and robotics.
  • New sections on transfer learning for deep learning in general and for natural language.
  • New sections on privacy, fairness, the future of work, and safe AI.
  • Extensive coverage of recent advances in AI applications.
  • Revised coverage of computer vision, natural language understanding, and speech recognition reflect the impact of deep learning methods on these fields.