The most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence The long-anticipated revision of Artificial Intelligence: A Modern Approach explores the full breadth and depth of the field of artificial intelligence (AI). The 4th Edition brings readers up to date on the latest technologies, presents concepts in a more unified manner, and offers new or expanded coverage of machine learning, deep learning, transfer learning, multiagent systems, robotics, natural language processing, causality, probabilistic programming, privacy, fairness, and safe AI.
The most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence The long-anticipated revision of Artificial Intelligence: A Modern Approach explores the full breadth and depth of the field of artificial intelligence (AI). The 4th Edition brings readers up to date on the latest technologies, presents concepts in a more unified manner, and offers new or expanded coverage of machine learning, deep learning, transfer learning, multiagent systems, robotics, natural language processing, causality, probabilistic programming, privacy, fairness, and safe AI.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
About our authors Stuart Russell was born in 1962 in Portsmouth, England. He received his B.A. with first-class 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 co-winner of the Computers and Thought Award. He is a Fellow of the American Association for Artificial Intelligence, the Association for Computing Machinery, and the American Association for the Advancement of Science, and Honorary Fellow of Wadham College, Oxford, and an Andrew Carnegie Fellow. He held the Chaire Blaise Pascal in Paris from 2012 to 2014. He has published over 300 papers on a wide range of topics in artificial intelligence. His other books include: The Use of Knowledge in Analogy and Induction, Do the Right Thing: Studies in Limited Rationality (with Eric Wefald), and Human Compatible: Artificial Intelligence and the Problem of Control. Peter Norvig is currently Director of Research at Google, Inc., and was the director responsible for the core Web search algorithms from 2002 to 2005. He is a Fellow of the American Association for Artificial Intelligence and the Association for Computing Machinery. Previously, he was head of the Computational Sciences Division at NASA Ames Research Center, where he oversaw NASA’s research and development in artificial intelligence and robotics, and chief scientist at Junglee, where he helped develop one of the first Internet information extraction services. He received a B.S. in applied mathematics from Brown University and a Ph.D. in computer science from the University of California at Berkeley. He received the Distinguished Alumni and Engineering Innovation awards from Berkeley and the Exceptional Achievement Medal from NASA. He has been a professor at the University of Southern California and a research faculty member at Berkeley. His other books are: Paradigms of AI Programming: Case Studies in Common Lisp, Verbmobil: A Translation System for Face-to-Face Dialog, and Intelligent Help Systems for UNIX. The two authors shared the inaugural AAAI/EAAI Outstanding Educator award in 2016.
Inhaltsangabe
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
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
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