"Fundamentals of the New Artificial Intelligence: Neural, Evolutionary, Fuzzy and More, " (2nd Ed.), Springer, Feb. 1, 2008   

 

Artificial intelligence—broadly defined as the study of making computers perform tasks that require human intelligence—has grown rapidly as a field of research and industrial application in recent years. Whereas traditionally, AI used techniques drawn from symbolic models such as knowledge-based and logic programming systems, interest has grown in newer paradigms, notably genetic algorithms, fuzzy logic, and neural networks.

 

The significantly updated 2nd edition of Fundamentals of the New Artificial Intelligence thoroughly covers the most essential and widely employed material pertaining to neural networks, genetic algorithms, fuzzy systems, rough sets, and chaos.  In particular, this unique textbook explores the importance of this content for real-world applications.  The exposition reveals the core principles, concepts, and technologies in a concise and accessible, easy-to-understand manner, and as a result, prerequisites are minimal: A basic understanding of computer programming and mathematics makes the book suitable for readers coming to this subject for the first time.

 

Topics and features:

 

  • Retains the well-received features of the first edition, yet clarifies and expands on the topic
  • Features completely new material on simulated annealing, Boltzmann machines, and extended fuzzy if-then rules tables [NEW]
  • Emphasizes the real-world applications derived from this important area of computer science
  • Provides easy-to-comprehend descriptions and algorithms
  • Updates all references, for maximum usefulness to professors, students, and other readers  [NEW]
  • Integrates all material, yet allows each chapter to be used or studied independently

 

This invaluable text and reference is an authoritative introduction to the subject and is therefore ideal for upper-level undergraduates and graduates studying artificial intelligence, soft computing, neural networks, evolutionary computing, and fuzzy systems.  In addition, the material is self-contained and therefore valuable to researchers in many related disciplines.  Professor Munakata is a leading figure in this field and has given courses on this topic extensively.

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ACM Computing Reviews, October, 2008

 

… This is an excellent textbook for undergraduate and graduate students in computer science, coming to this subject for the first time and desiring to acquire a comprehensive view of the whole area of soft computing. The mathematical background required is minimal … . Critical comparisons among the models illustrated are suggested, and essential literature references are given for further reading.

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Errata (2nd Ed.)

 

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"Fundamentals of the New Artificial Intelligence: Beyond Traditional Paradigms, " (1st Ed.), Springer-Verlag, 1998  

Artificial intelligence has grown and diversified rapidly as a field of research and industrial application in recent years. In particular, non-traditional, non-symbolic domains of the field have attracted much interest. These new domains include neural networks, genetic algorithms, fuzzy logic, rough set theory, and chaotic systems. This book covers the most fundamental and widely used material in these new technologies at a fast pace and in an easily understandable fashion. It also introduces the basic ideas of how these technologies may be employed for real-world applications. The book can be used as a textbook or reference for computer science, artificial intelligence, or related disciplines such as engineering, social science, business, and medicine.

Professor Toshinori Munakata is a leading figure in this field and has given courses on this topic extensively. As a result, students and researchers will enjoy this authoritative introduction to the subject.
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amazon.com Customer Rating:

  Very good Non-Symbolic AI Overview and Introduction, May 10, 2000. Reviewer: Dieter Wimberger from Leoben, Austria

If you are searching for an overview of Non-Symbolic AI fields with introduction (not just shallow), hints and examples for practical application, and comparison of performance, strengths and weaknesses, this is definitely a book to be considered. It covers the topics pretty well, and only the chapters about rough sets and chaos approaches seem to differ a bit from the scheme followed in the chapters before.
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Errata (1st Ed.)
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