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.
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IntroductionIntelligent AgentsSolving Problems by SearchingSearch in Complex EnvironmentsAdversarial Search and GamesConstraint Satisfaction ProblemsLogical AgentsFirst-Order LogicInference in First-Order LogicKnowledge RepresentationAutomated PlanningQuantifying UncertaintyProbabilistic ReasoningProbabilistic Reasoning over TimeProbabilistic ProgrammingMaking Simple DecisionsMaking Complex DecisionsMultiagent Decision MakingLearning from ExamplesLearning Probabilistic ModelsDeep LearningReinforcement LearningNatural Language ProcessingDeep Learning for Natural Language ProcessingRoboticsPhilosophy and Ethics of AIThe Future of AI
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Hallmark features of this title Nontechnical learning material introduces major concepts using intuitive explanations, before going into mathematical or algorithmic details. The nontechnical language makes this book accessible to a broader range of readers.A unified approach to AI clearly details how the various subfields of AI fit together to build actual, useful programs.In-depth coverage of both basic and advanced topics provides students with a solid understanding of the frontiers of AI without compromising complexity and depth.The author-maintained website at http://aima.cs.berkeley.edu/ includes video tutorials, interactive student exercises, and supplemental coding examples and applications in Python, Java and Javascript.
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New and updated features of this title NEW: Chapters in this edition 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).UPDATED: Definition of AI systems is generalized to assume that the intelligent agent may be uncertain about the true objectives of the human(s) on whose behalf it operates.UPDATED: Machine learning coverage. Robotics material is significantly enhanced to include robots that interact with humans and the application of reinforcement learning to robotics.NEW: Sections address topics such as causality (by Judea Pearl); Monte Carlo search for games and robotics; transfer learning for deep learning in general and for natural language; privacy; fairness; the future of work; and safe AI.NEW: Recent advances in AI applications receive extensive coverage.UPDATED: Coverage of the impact of deep learning methods on the fields of computer vision, natural language understanding, and speech recognition.
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Produktdetaljer

ISBN
9780134610993
Publisert
2020-11-10
Utgave
4. utgave
Utgiver
Vendor
Pearson
Vekt
100 gr
Høyde
100 mm
Bredde
100 mm
Dybde
100 mm
Aldersnivå
U, 05
Språk
Product language
Engelsk
Format
Product format
Heftet
Antall sider
1136

Biographical note

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.