• Nontechnical learning material introduces major concepts using intuitive explanations, before going into mathematical or algorithmic details. The nontechnical language makes the book accessible to a broader range of readers.

• A unified approach to AI shows students how the various subfields of AI fit together to build actual, useful programs.

• The basic definition of AI systems is generalised 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.

• 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.

• 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 field

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 http://aima.cs.berkeley.edu/ 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 nforcement learning to robotics.

• New section on causalityby 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

• 1. Introduction

• 2. Intelligent Agents

• 3. Solving Problems by Searching

• 4. Search in Complex Environments

• 5. Adversarial Search and Games

• 6. Constraint Satisfaction Problems

• 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. Probabilistic Programming

• 16. Making Simple Decisions

• 17. Making Complex Decisions

• 18. Multiagent Decision Making

• 19. Learning from Examples

• 20. Learning Probabilistic Models

• 21. Deep Learning

• 22. Reinforcement Learning

• 23. Natural Language Processing

• 24. Deep Learning for Natural Language Processing

• 25. Robotics

• 26. Philosophy and Ethics of AI

• 27. The Future of AI