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Artificial Intelligence: A Modern Approach, 3e offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence.
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Artificial intelligence: A Modern Approach, 3rd edition, by Russell is ideal for one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence.
Number one in its field, this textbook is ideal for one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence.
Artificial intelligence: A Modern Approach 3rd edition is also a valuable resource for computer professionals, linguists, and cognitive scientists interested in artificial intelligence. The revision of this best-selling text offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence.
Peter Norvig, contributing Artificial Intelligence author and Professor Sebastian Thrun, a Pearson author are offering a free online course at Stanford University on artificial intelligence.
This publication is protected byCopyright and permissions should be obtained from the publisher prior to any prohibited reproduction, storage in a retrieval system, or transmission in any form or by any means, electronic, mechanical, photocopying, recording. Upper Saddle River, NI 07458.The author and publisher of this book have used their best efforts in preparing this hook. More detailed descriptions of algorithms for probabilistic inference, fast propositional inference, probabilistic learning approaches including EM, and other topics.

These efforts include the development, research, and testing of the theories and programs to determine their effectiveness.
The author and publisher make no warranty of any kind, expressed or implied, with regard to these programs or the documentation contained in this book. We apologize to those whose subfields are, as a result, less recognizable.New to this editionThis edition captures the changes in Al that have taken place since the last edition in 2003. There have been important applications of AI technology, such as the widespread deployment of practical speech recognition, machine translation autonomous vehicles, and household robotics. And there has been a great deal of theoretical progress, particularly in areas such as probabilistic reasoning, machine learning, and computer vision. Most important from our point of view is the continued evolution in how we think about the field, and thus how we organize the book. The major changes are as follows:a€?We place more emphasis on partially observable and nondeterministic environments, especially in the nonprobabilistic settings of search and planning. The remaining SO% reflects older work but has been largely rewritten to present a more unified picture of the field.viiviii PrefaceOverview of the bookThe main unifying theme is the idea of an intelligent agent. We define Al as the study of agents that receive percepts from the environment and perform actions. Each such agent implements a function that maps percept sequences to actions, and we cover different ways to represent these functions, such as reactive agents, real-time planners, and decision-theoretic systems.

We explain the role of learning as extending the reach of the designer into unknown environments, and we show how that role constrains agent design, favoring explicit knowl- edge representation and reasoning.
We treat robotics and vision not as independently defined problems, but as occurring in the service of achievinggoals. We stress the importance of the task environment in determining the appropriate agent design. Our primary aim is to convey the ideas that have emerged over the past fifty years of Al research and the past two millennia of related work. We have tried to avoid excessive formality in the presentation of these ideas while retaining precision.
A one-semester course can use selected chapters to suit the interests of the instructor and students.

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