Items related to Artificial Intelligence: Theory and Practice
Synopsis
This book provides a detailed understanding of the broad issues in artificial intelligence and a useful survey of current AI technology. The author delivers broad coverage of innovative representational techniques, including neural networks, image processing, and probabilistic reasoning, alongside the traditional methods of symbolic reasoning. AI algorithms are described in detailed prose in the text and fully implemented in LISP at the ends of chapters. A stand-alone LISP chapter makes an excellent reference and refresher. Each chapter includes a detailed description of an AI application.
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From the Back Cover
Emphasize both scientific theory and practical applications with Artifical Intelligence: Theory and Practice, a new text by Thomas Dean with James Allen and Yiannis Aloimonos. This modern, balanced introduction to artificial intelligence examines both the representational and computational issues that arise in developing systems capable of machine intelligence. The authors discuss these issues in terms of their syntax, semantics, and computation and offer detailed coverage of both traditional symbolic reasoning techniques and alternative techniques such as neural networks, probabilistic reasoning, and image processing. In addition, they emphasize the role of experimental computer science by showing the practical implementation of algorithms by first using pseudocode and then LISP code.
To ensure that readers fully understand the topic and its applications, the authors provide motivating examples throughout. AI in Practice boxes appear in each chapter, demonstrating real-world uses of artificial intelligence by NASA, General Motors Corporation, Microsoft Corporation, and other companies. LISP Implementation appendices are found at the end of most chapters, providing fully-documented implementations of important algorithms (also available in Scheme and C++ implementations via ftp). These are carefully coordinated with the discussions in the chapters making it easy for students to complete computational experiments. Plus, the text features summaries, exercises, and background sections describing related work at the end of each chapter.
0805325476B04062001
About the Author
James Allen is the John H. Dessaurer Professor of Computer Science at the University of Rochester. He has taught natural language processing to undergraduate and graduate students for 14 years. He is a fellow of the AAAI and was the recipient of the Presidential Young Investigator Award (1985-1989). In addition, Professor Allen was the Editor-in- Chief of Computational Linguistics from 1983-1993.
0805325476AB04062001
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