Artificial Intelligence and Soft Computing - Info and Reading Options

Google Books:

With all the material available in the field of artificial intelligence (AI) and soft computing-texts, monographs, and journal articles-there remains a serious gap in the literature. Until now, there has been no comprehensive resource accessible to a broad audience yet containing a depth and breadth of information that enables the reader to fully understand and readily apply AI and soft computing concepts. Artificial Intelligence and Soft Computing fills this gap. It presents both the traditional and the modern aspects of AI and soft computing in a clear, insightful, and highly comprehensive style. It provides an in-depth analysis of mathematical models and algorithms and demonstrates their applications in real world problems. Beginning with the behavioral perspective of "human cognition," the text covers the tools and techniques required for its intelligent realization on machines. The author addresses the classical aspects-search, symbolic logic, planning, and machine learning-in detail and includes the latest research in these areas. He introduces the modern aspects of soft computing from first principles and discusses them in a manner that enables a beginner to grasp the subject. He also covers a number of other leading aspects of AI research, including nonmonotonic and spatio-temporal reasoning, knowledge acquisition, and much more. Artificial Intelligence and Soft Computing: Behavioral and Cognitive Modeling of the Human Brain is unique for its diverse content, clear presentation, and overall completeness. It provides a practical, detailed introduction that will prove valuable to computer science practitioners and students as well as to researchers migrating to the subject from other disciplines.

Open Data:

Contents -- Chapter 1 Introduction to Artificial Intelligence and Soft Computing -- Chapter 2 The Psychological Perspective of Cognition -- Chapter 3 Production Systems -- Chapter 4 Problem Solving by Intelligent Search -- Chapter 5 The Logic of Propositions and Predicates -- Chapter 6 Principles in Logic Programming -- Chapter 7 Default and Non-Monotonic Reasoning -- Chapter 8 Structured Approach to Knowledge Representation -- Chapter 9 Dealing with Imprecision and Uncertainty -- Chapter 10 Structured Approach to Fuzzy Reasoning -- Chapter 11 Reasoning with Space and Time -- Chapter 12 Intelligent Planning -- Chapter 13 Machine Learning Techniques -- Chapter 14 Machine Learning Using Neural Nets -- Chapter 15 Genetic Algorithms -- Chapter 16 Realizing Cognition Using Fuzzy Neural Nets -- Chapter 17 Visual Perception -- Chapter 18 Linguistic Perception -- Chapter 19 Problem Solving by Constraint Satisfaction -- Chapter 20 Acquisition of Knowledge -- Chapter 21 Validation, Verification and Maintenance Issues -- Chapter 22 Parallel and Distributed Architecture for Intelligent Systems -- Chapter 23 Case Study I -- Chapter 24 Case Study II -- Chapter 24 The Expectations from the Readers -- Appendix A How to Run the Sample Programs? -- Appendix B Derivation of the Back-propagation Algorithm -- Appendix C Proof of the Theorems of Chapter 10

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