Intelligent Systems for Engineers and Scientists, Second Edition

Published:
October 11, 2000 by CRC Press - 488 Pages
Author(s):
Adrian A. Hopgood

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Hardback
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ISBN 9780849304569
Cat# 0456
 

Features

  • Presents symbolic knowledge-based techniques - such as rules, objects, frames, intelligent agents, and learning systems
  • Describes numerical techniques - such as neural networks, Bayesian updating, certainty theory, fuzzy logic, and optimization algorithms, including genetic algorithms
  • Introduces autonomous agents as the next logical step following object orientation
  • Includes detailed discussion of techniques for applying intelligent systems to a range of practical problems
  • Incorporates several worked examples and case studies from science and engineering
  • Provides new chapters on neural networks, optimization algorithms, intelligent agents, and hybrid systemsAppropriate as a senior undergraduate or postgraduate text for courses in Artificial Intelligence for Technology, Computational Intelligence and Knowledge Engineering, and Knowledge-Based Expert Systems for Engineering Projects.

    • Summary

    This updated version of the best-selling Knowledge-Based Systems for Engineers and Scientists (CRC Press, 1993) embraces both the explicit knowledge-based models retained from the first edition and the implicit numerical models represented by neural networks and optimization algorithms. The title change to Intelligent Systems for Engineers and Scientists reflects its broader scope, incorporating knowledge-based systems, computational intelligence, and their hybrids.

    Clear and concise, the book shows the issues encountered in the development of applied systems and describes a wide range of intelligent systems techniques. The author describes each technique at the level of detail required to develop intelligent systems for real applications. Whether you are building intelligent systems or you simply want to know more about them, Intelligent Systems for Engineers and Scientists provides you with a detailed, up-to-date, and practical guide to solving real problems in science and engineering.

    This indispensable book provides everything in one volume:

    BREADTH - from knowledge-based systems to computational intelligence
    DEPTH - from introductory concepts to advanced specialist techniques
    SCOPE - from principles to practicalities

    Table of Contents

    INTRODUCTION
    Intelligent Systems
    Knowledge-Based Systems
    The Knowledge Base
    Deduction, Abduction, and Induction
    The Inference Engine
    Declarative and Procedural Programming
    Expert Systems
    Knowledge Acquisition
    Search
    Computational Intelligence
    Integration with other Software
    RULE-BASED SYSTEMS
    Rules and Facts
    A Rule-Based System for Boiler Control
    Rule Examination and Rule Firing
    Maintaining Consistency
    The Closed-World Assumption
    Use of Variables within Rules
    Forward-Chaining
    Conflict Resolution
    Backward-Chaining
    A Hybrid Strategy
    Explanation Facilities
    DEALING WITH UNCERTAINTY
    Sources of Uncertainty
    Bayesian Updating
    Certainty Theory
    Fuzzy Logic
    Other Techniques
    OBJECT-ORIENTED SYSTEMS
    Objects and Frames
    An Illustrative Example
    Introducing OOP
    Data Abstraction
    Inheritance
    Encapsulation
    Unified Modeling Language (UML)
    Dynamic (or late) Binding
    Message Passing and Function Calls
    Type Checking
    Further Aspects of OOP
    Frame-Based Systems
    INTELLIGENT AGENTS
    Characteristics of an Intelligent Agent
    Agents and Objects
    Agent Architectures
    Multiagent Systems
    SYMBOLIC LEARNING
    Introduction
    Learning by Induction
    Case-Based Reasoning
    OPTIMIZATION ALGORITHMS
    Optimization
    The Search Space
    Searching the Search Space
    Hill-Climbing and Gradient Descent Algorithms
    Simulated Annealing
    Genetic Algorithms
    NEURAL NETWORKS
    Introduction
    Neural Network Applications
    Nodes and Interconnections
    Single and Multilayer Perceptrons
    The Hopfield Network
    MAXNET
    The Hamming Network
    Adaptive Resonance Theory (ART) Networks
    Kohonen Self-Organizing Networks
    Radial Basis Function Networks
    HYBRID SYSTEMS
    Convergence of Techniques
    Blackboard Systems
    Genetic-Fuzzy Systems
    Neuro-Fuzzy Systems
    Genetic Neural Systems
    Clarifying and Verifying Neural Networks
    Learning Classifier Systems
    TOOLS AND LANGUAGES
    A Range of Intelligent Systems Tools
    Expert System Shells
    Toolkits and Libraries
    Artificial Intelligence Languages
    Lisp
    Prolog
    Comparison of AI Languages
    SYSTEMS FOR INTERPRETATION AND DIAGNOSIS
    Introduction
    Deduction and Abduction for Diagnosis
    Depth of Knowledge
    Model-Based Reasoning
    Case Study: A Blackboard System for Interpreting Ultrasonic Images
    SYSTEMS FOR DESIGN AND SELECTION
    The Design Process
    Design as a Search Problem
    Computer Aided Design
    The Product Design Specification (PDS)
    Conceptual Design
    Constraint Propagation and Truth Maintenance
    Case Study: The Design of a Lightweight Beam
    Design as a Selection Exercise
    Failure Mode and Effects Analysis (FMEA)
    SYSTEMS FOR PLANNING
    Introduction
    Classical Planning Systems
    STRIPS
    Considering the Side Effects of Actions
    Hierarchical Planning
    Postponement of Commitment
    Job-Shop Scheduling
    Constraint-Based Analysis
    Replanning and Reactive Planning
    SYSTEMS FOR CONTROL
    Introduction
    Low-Level Control
    Requirements of High-Level (Supervisory) Control
    Blackboard Maintenance
    Time-Constrained Reasoning
    Fuzzy Control
    The BOXES Controller
    Neural Network Controllers
    Statistical Process Control (SPC)
    CONCLUDING REMARKS
    Benefits
    Information
    Trends
    INDEX

    Editorial Reviews

    "…it has a clear and concise style of presentation but still manages to comprise a great deal of material. … The new chapters on intelligent agents, neural networks and optimisation algorithms fit neatly alongside the established chapters. I read the first edition of Adrian Hopgood's book a few years ago and have consulted it on many occasions for AI projects I have been involved with. I fully expect to make use of this new edition of Intelligent Systems for Engineers and Scientists for future projects."
    - Desmond Case, Senior Lecturer, University College Northampton, in Expert Systems: The International Journal of Knowledge Engineering and Neural Networks, September 2002

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