Artificial Intelligence and Neural Networks: Steps Toward Principled Integration (Neural Networks: Foundations to Applications) [Hardcover]

Vasant Honavar (Editor), Leonard Uhr (Editor)

Book Description

ISBN-10: 0123550556 | ISBN-13: 978-0123550552 | Publication Date: November 24, 1994

Traditional artificial intelligence and neural networks are generally considered appropriate for solving different types of problems. On the surface, these two approaches appear to be very different, but a growing body of current research is focused on how the strengths of each can be incorporated into the other and built into systems that include the best features of both.

Artificial Intelligence and Neural Networks: Steps Toward Principled Integration is a critical examination of the key issues, underlying assumptions, and suggestions related to the reconciliation and principled integration of artificial intelligence and neural networks. With contributions from leading researchers in the field, this comprehensive text provides a thorough introduction to the basics of symbol processing, connectionist networks, and their integration. Numerous examples of the integration of artificial intelligence and neural networks for a variety of specific applications provide unique insight into this evolving area.

Includes contributions from some of the leading researchers in this area
Provides a complete introduction to the basics of symbol processing, connectionist networks, and their integration
Includes examples of the integration of artificial intelligence and neural networks for a variety of specific applications, including vision and pattern recognition

Product Details

• Hardcover: 653 pages
• Publisher: Academic Press (November 24, 1994)
• Language: English
• ISBN-10: 0123550556
• ISBN-13: 978-0123550552
• Product Dimensions: 9.5 x 6.2 x 1.5 inches
• Shipping Weight: 2.8 pounds
• Average Customer Review: 5.0 out of 5 stars  See all reviews (2 customer reviews)
• Amazon Best Sellers Rank: #6,662,669 in Books (See Top 100 in Books)

Customer Reviews

Most Helpful Customer Reviews

4 of 5 people found the following review helpful:

5.0 out of 5 stars A modern synthesis of approaches in Artificial Intelligence, June 16, 2001

By A Customer

This review is from: Artificial Intelligence and Neural Networks: Steps Toward Principled Integration (Neural Networks: Foundations to Applications) (Hardcover)

This book is an excellent reference book on current approaches to some of the foundational questions in artificial intelligence, philosophy of mind, and engineering of intelligent systems. The editors as amply demonstrated by their other publications, have the rare ability to interrelate an amazing diversity of perspectives on Artificial Intelligence - from symbolic methods to neural networks and evolutionary apporaches, to uncover the shared principles and common foundations, and show how different paradigms can be brought together in synergistic ways to advance our ability to design and analyze intelligent systems. This book includes chapters written by some of the leading experts in the field. The book should be especially useful as a reference to graduate students and researchers interested in artificial intelligence, machine learning, cognitive science, software agents, and philosophy of mind. It should also be useful as supplementary reading for a graduate or upper level undergraduate course in artificial intelligence or cognitive science, or as a primary text for a seminar course. All in all, a great book!

4 of 5 people found the following review helpful:

5.0 out of 5 stars Excellent Persepctive on Connectionist/Symbolic Debate in AI, December 10, 2000

By A Customer

This review is from: Artificial Intelligence and Neural Networks: Steps Toward Principled Integration (Neural Networks: Foundations to Applications) (Hardcover)

Symbolic (e.g., logic based) and connectionist (e.g., neural network) models are often viewed as separate, and perhaps incompatible approaches to artificial intelligence and cognitive modelling. Far too many young researchers (including myself) have been swept away by the exaggerated claims of each camp. This collection of chapters is an excellent medicine for such a malaise. The chapters in this collection demonstrate, with pursuasive theoretical and philosophical arguments as well as empirical data that symbolism and connectionism can, and perhaps should, be reconciled. At the time this book came out, this was not a popular position. However, recent developments in AI and cognitive science have more than vindicated the views expressed in this book. Now (6 years later), some of the chapters are a bit dated. However, I recommend the book strongly to those who are interested in foundational issues in artificial intelligence and cognitive science.