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Artificial Intelligence: A Modern Approach

By: Contributor(s): Material type: TextTextPublication details: Chennai Pearson 2022Edition: 4th EditionDescription: 1288pISBN:
  • 9789356063570
DDC classification:
  • 006.3 RUS
Summary: 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)
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Stuart Russell has received his B.A. with firstclass honours in physics from Oxford University in 1982, and his Ph.D. in computer science from Stanford in 1986. He then joined the faculty of the University of California at Berkeley, where he is a professor and former chair of computer science, director of the Center for Human-Compatible AI, and holder of the Smith–Zadeh Chair in Engineering. In 1990, he received the Presidential Young Investigator Award of the National Science Foundation, and in 1995 he was cowinner of the Computers and Thought Award. Peter Norvig is currently a Director of Research at Google, Inc., and was previously the director responsible for the core Web search algorithms. He co-taught an online AI class that signed up 160,000 students, helping to kick off the current round of massive open online classes. He received a B.S. in applied mathematics from Brown University and a Ph.D. in computer science from Berkeley. He has been a professor at the University of Southern California and a faculty member at Berkeley and Stanford. The two authors shared the inaugural AAAI/EAAI Outstanding Educator award in 2016.


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)

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