Synopses & Reviews
Synopsis
This book was originally titled "Fundamentals of the New Artificial Intelligence: Beyond Traditional Paradigms." I have changed the subtitle to better represent the contents of the book. The basic philosophy of the original version has been kept in the new edition. That is, the book covers the most essential and widely employed material in each area, particularly the material important for real-world applications. Our goal is not to cover every latest progress in the fields, nor to discuss every detail of various techniques that have been developed. New sections/subsections added in this edition are: Simulated Annealing (Section 3.7), Boltzmann Machines (Section 3.8) and Extended Fuzzy if-then Rules Tables (Sub-section 5.5.3). Also, numerous changes and typographical corrections have been made throughout the manuscript. The Preface to the first edition follows. General scope of the book Artificial intelligence (AI) as a field has undergone rapid growth in diversification and practicality. For the past few decades, the repertoire of AI techniques has evolved and expanded. Scores of newer fields have been added to the traditional symbolic AI. Symbolic AI covers areas such as knowledge-based systems, logical reasoning, symbolic machine learning, search techniques, and natural language processing. The newer fields include neural networks, genetic algorithms or evolutionary computing, fuzzy systems, rough set theory, and chaotic systems.
Synopsis
Neural Networks: Fundamentals and the Backpropagation Model.- Neural Networks: Other Models.- Genetic Algorithms and Evolutionary Computing.- Fuzzy Systems.- Rough Sets.- Chaos.
Synopsis
Artificial intelligence has grown rapidly as a field of research and industrial application in recent years. Whereas traditionally, AI used techniques drawn from rule-based and logic programming systems, interest is now growing in less precise heuristic methods, notably genetic algorithms, fuzzy logic, and neural networks. The second edition of this textbook covers these new technologies and how they may be applied. It provides an accessible introduction to AI suitable for readers coming to this subject for the first time. In each topic, the book covers the most essential and widely employed material, particularly as it is used in real-world applications. Its emphasis is on concise yet clear descriptions of the technical substance.
Professor Munakata is a leading figure in this field and has given courses on this topic extensively. Students and researchers will enjoy this authoritative introduction to the subject.
Synopsis
The book covers the most essential and widely employed material in each area, particularly the material important for real-world applications. Our goal is not to cover every latest progress in the fields, nor to discuss every detail of various techniques that have been developed.
New sections/subsections added in this edition are: Simulated Annealing (Section 3.7), Boltzmann Machines (Section 3.8) and Extended Fuzzy if-then Rules Tables (Sub-section 5.5.3). Also, numerous changes and typographical corrections have been made throughout the manuscript. The Preface to the first edition follows. General scope of the book Artificial intelligence (AI) as a field has undergone rapid growth in diversification and practicality. For the past few decades, the repertoire of AI techniques has evolved and expanded. Scores of newer fields have been added to the traditional symbolic AI. Symbolic AI covers areas such as knowledge-based systems, logical reasoning, symbolic machine learning, search techniques, and natural language processing. The newer fields include neural networks, genetic algorithms or evolutionary computing, fuzzy systems, rough set theory, and chaotic systems.