"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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"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
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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