Luger, George F. Artificial intelligence: structures and strategies for complex problem solving / George F. Luger 6th ed. p. cm. Includes. The right of George F. Luger to be identified as author of this work has been asserted These remain central to the practice of artificial intelligence, and required a 6e first time that state is discovered, when a state is encountered a second. This free book captures the essence of artificial intelligence solving the complex ); eBook (5th Edition); Paperback pages; eBook PDF; Language: English George F. Luger has been a Professor in the University of New Mexico, .
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pages, George F. Luger, , , Pearson Ideal for an undergraduate course in AI, the Sixth Edition presents the fundamental . com//06/bestthing.info 1 George F Luger ARTIFICIAL INTELLIGENCE 6th edition Structures and Strategies for Complex Problem Solving HEURISTIC SEARCH Luger: Artificial. Artificial Intelligence: Structures And Strategies For Complex Problem Solving Author: George F. Luger | William A. Stubblefield Sixth Edition. Read more.
Artificial Intelligence: George F.
Luger, University of New Mexico. If You're a Student download this product Additional order info.
Description In this accessible, comprehensive text, George Luger captures the essence of artificial intelligence—solving the complex problems that arise wherever computer technology is applied. Ideal for an undergraduate course in AI, the Sixth Edition presents the fundamental concepts of the discipline first then goes into detail with the practical information necessary to implement the algorithms and strategies discussed.
Preface Preface is available for download in PDF format.
Accessible presentation: The combination of a thorough and balanced treatment of the theoretical foundations of intelligent problem solving with the data structures and algorithms needed for implementation provides a holistic picture for students. AI foundations: A unique discussion of the history of AI and social and the associated philosophical issues is presented in the early chapters.
Applied programming languages: Applications in context: The practical applications of AI are put into context using model-based reasoning and planning examples from the NASA space program.
Coverage of the stochastic methodology: Stochastic natural language processing, including finite state machines, dynamic programming, and the Viterbi algorithm, is integrated into introductory chapters.
Expanded stochastic approaches to reasoning in uncertain situations, including Bayesian belief networks and Markov models, are discussed in Chapter 9.
New for the Sixth Edition, Chapter 13, Probabilistically Based Machine Learning, covers stochastic methods that support machine learning. New to This Edition.
Artificial Intelligence: Structures and Strategies for Complex Problem Solving, 6th Edition
Presentation of agent technology and the use of ontologies are added to Chapter 7, Knowledge Presentation. A new machine-learning chapter, based on stochastic methods, Chapter 13, Probabilistically-Based Machine Learning. This new chapter covers stochastic approaches to machine learning, including first-order Bayesian networks, variants of hidden Markov models, inference with Markov random fields and loopy belief propagation.
Presentation of parameter fitting with expectation maximization learning and structure learning using Markov chain Monte Carlo sampling.
Use of Markov decision processes in reinforcement learning. Natural language processing with dynamic programming the Earley parser and other probabilistic parsing techniques including Viterbi, are added to Chapter 15, Understanding Natural Language.
A new supplemental programming book is available: Available online and in print, this book demonstrates these languages as tools for building many of the algorithms presented throughout Luger's AI book.
References and citations are updated throughout. Attitudes toward Intelligence, Knowledge, and Human Artifice 3 1.
A Logic-Based Financial Advisor 73 2. A Network Language 7.
Alternatives to Logic 9. An Analysis Chapter 3 uninformed search project 1 and 2 Chapter 4 heuristic search. Biointelligence Lab School of Computer Sci. Similar presentations.
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University of Central Arkansas Spring Notes 6: Please wait OK Heuristic Search In addition to depth-first search, breadth-first search, bound depth-first search, and iterative deepening, we can also use informed or. About project SlidePlayer Terms of Service.Please try again.
Artificial Intelligence, 6th edition. Previous editions.
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