Artifical Intelligence: Structures and Strategies for Complex Problem Solving | KitaabNow

Artifical Intelligence: Structures and Strategies for Complex Problem Solving

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  • Author: George F. Luger
  • ISBN: 9789673498178
  • Publisher: Pearson Education
  • Edition: 5th
  • Format: Paperback – 902 pages
  • Language: English

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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. Students learn how to use a number of different software tools and techniques to address the many challenges faced by today’s computer scientists.

Artificial Intelligence: Structures and Strategies for Complex Problem Solving is ideal for a one or two-semester undergraduate course on AI.

Key Features
  • 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: Example programs are written in three programming languages, Prolog, Lisp, and Java™ in Chapters 17–19, which are available on the open-access Companion Website. These chapters show students the power of these languages and demonstrate how they can be used to create the data structures of the AI book that support “intelligent” problem solving.
  • Applications in context: The practical applications of AI are put into context using model-based reasoning and planning examples from the NASA space program. Comments on the AI endeavor from the perspectives of philosophy, psychology and neuro-physiology give students a holistic picture of AI’s application in the real world.
  • 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.
Author Description

George Luger is currently a Professor of Computer Science, Linguistics, and Psychology at the University of New Mexico. He received his PhD from the University of Pennsylvania and spent five years researching and teaching at the Department of Artificial Intelligence at the University of Edinburgh.

Additional information
Weight 1.450 kg
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