Intelligent Systems: Principles, Paradigms and Pragmatics

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Artificial Intelligence has changed significantly in recent years and many new resources and approaches are now available to explore and implement this important technology. Intelligent Systems: Principles, Paradigms, and Pragmatics takes a modern, 21st-century approach to the concepts of Artificial Intelligence and includes the latest developments, developmental tools, programming, and approaches related to AI. The author is careful to make the important distinction between theory and practice, and focuses on a broad core of technologies, providing students with an accessible and comprehensive introduction to key AI topics. Key Features Provides students with a full understanding of Intelligent Systems (IS) by including key in formation on such important topics as Logic, Production Systems, Ontologies, Fuzzy Systems, Genetic Algorithms, IS programming and more. Conveys a technical perspective, giving readers the ability to determine when an IS approach is appropriate for a given problem, and then selecting and implementing a suitable IS method. Includes hands-on examples and exercises allowing readers to implement real-world IS technologies. Provides a current look at off-the-shelf tools for developing production systems and ontologies. Discusses the technology and pragmatics of embedded IS software. Provides comprehensive references to both seminal work and current implementation strategies.

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Robert J. Schalkoff is currently Professor of Electrical and Computer Engineering at Clemson University. His primary scholarly interests are in intelligent systems and computing. He is also the author of Programming Languages and Methodologies, Digital Image Processing and Computer Vision, Artificial Intelligence: An Engineering Approach, Pattern Recognition: Statistical, Syntactic and Neural Approaches, and Artificial Neural Networks. He received his Ph.D. in Electrical Engineering from the University of Virginia. Table of Contents Introduction to Intelligent Systems First Steps in IS: Representation, Ontologies, and Obtaining Expertise Search and Computational Complexity in IS Constraint Satisfaction Problems, Part 1 CSPs, Part 2: Structural Approaches Leading to Natural Language (NL) Understanding and Related Topics From Logic-Based Chaining to Production Systems The c Language Integrated Production System (clips) Extended and Structured Production System Representation and Manipulation Approaches, Including Agents Soar Representing and Manipulating Uncertainty in IS, Part1: Confidence Factors, Probability, Belief Networks and Multivalued Logic Representing and Manipulating Uncertainty in IS, Part 2: Fuzzy Systems and FUZZYclips Planning in IS Biologically-inspired Computing and IS: Neural Networks (Part 1) Neural Networks (Part 2): Recurrent Networks and IS Applications Neural Networks (Part 3): Self-Organizing Systems Learning in IS Genetic Algorithms, Swarm Intelligence and Other Evolutionary Computing Concepts in IS.