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3rd Edition

Intelligent Systems for Engineers and Scientists





ISBN 9781138374287
Published September 10, 2018 by CRC Press
180 B/W Illustrations

 
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Book Description

The third edition of this bestseller examines the principles of artificial intelligence and their application to engineering and science, as well as techniques for developing intelligent systems to solve practical problems. Covering the full spectrum of intelligent systems techniques, it incorporates knowledge-based systems, computational intelligence, and their hybrids.

Using clear and concise language, Intelligent Systems for Engineers and Scientists, Third Edition features updates and improvements throughout all chapters. It includes expanded and separated chapters on genetic algorithms and single-candidate optimization techniques, while the chapter on neural networks now covers spiking networks and a range of recurrent networks. The book also provides extended coverage of fuzzy logic, including type-2 and fuzzy control systems. Example programs using rules and uncertainty are presented in an industry-standard format, so that you can run them yourself.

The first part of the book describes key techniques of artificial intelligence—including rule-based systems, Bayesian updating, certainty theory, fuzzy logic (types 1 and 2), frames, objects, agents, symbolic learning, case-based reasoning, genetic algorithms, optimization algorithms, neural networks, hybrids, and the Lisp and Prolog languages. The second part describes a wide range of practical applications in interpretation and diagnosis, design and selection, planning, and control.

The author provides sufficient detail to help you develop your own intelligent systems for real applications. Whether you are building intelligent systems or you simply want to know more about them, this book provides you with detailed and up-to-date guidance.

Check out the significantly expanded set of free web-based resources that support the book at: http://www.adrianhopgood.com/aitoolkit/

Table of Contents

Introduction
Intelligent Systems
A Spectrum of Intelligent Behavior
Knowledge-Based Systems
The Knowledge Base
     Rules and Facts
     Inference Networks
     Semantic Networks
Deduction, Abduction, and Induction
The Inference Engine
Declarative and Procedural Programming
Expert Systems
Knowledge Acquisition
Search
Computational Intelligence
Integration with Other Software
Further Reading

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 Local Variables within Rules
Forward Chaining (a Data-Driven Strategy)
     Single and Multiple Instantiation of Local Variables
     Rete Algorithm
Conflict Resolution
     First Come, First Served
     Priority Values
     Metarules
Backward Chaining (a Goal-Driven Strategy)
     The Backward-Chaining Mechanism
     Implementation of Backward Chaining
     Variations of Backward Chaining
     Format of Backward-Chaining Rules
A Hybrid Strategy
Explanation Facilities
Summary
Further Reading

Handling Uncertainty: Probability and Fuzzy Logic
Sources of Uncertainty
Bayesian Updating
     Representing Uncertainty by Probability
     Direct Application of Bayes’ Theorem
     Likelihood Ratios
     Using the Likelihood Ratios
     Dealing with Uncertain Evidence
     Combining Evidence
     Combining Bayesian Rules with Production Rules
     A Worked Example of Bayesian Updating
     Discussion of the Worked Example
     Advantages and Disadvantages of Bayesian Updating
Certainty Theory
     Introduction
     Making Uncertain Hypotheses
     Logical Combinations of Evidence
          Conjunction
          Disjunction
          Negation
     A Worked Example of Certainty Theory
     Discussion of the Worked Example
     Relating Certainty Factors to Probabilities
Fuzzy Logic: Type-1
     Crisp Sets and Fuzzy Sets
     Fuzzy Rules
     Defuzzification
          Stage 1: Scaling the Membership Functions
          Stage 2: Finding the Centroid
     Defuzzifying at the Extremes
     Sugeno Defuzzification
     A Defuzzification Anomaly
Fuzzy Control Systems
     Crisp and Fuzzy Control
     Fuzzy Control Rules
     Defuzzification in Control Systems
Fuzzy Logic: Type-2
Other Techniques
     Dempster–Shafer Theory of Evidence
     Inferno
Summary
Further Reading

Agents, Objects, and Frames
Birds of a Feather: Agents, Objects, and Frames
Intelligent Agents
Agent Architectures
     Logic-Based Architectures
     Emergent Behavior Architectures
     Knowledge-Level Architectures
      Layered Architectures
Multiagent Systems
     Benefits of a Multiagent System
     Building a Multiagent System
     Contract Nets
     Cooperative Problem-Solving (CPS)
     Shifting Matrix Management (SMM)
     Comparison of Cooperative Models
     Communication between Agents
Swarm Intelligence
Object-Oriented Systems
      Introducing OOP
     An Illustrative Example
     Data Abstraction
          Classes
          Instances
          Attributes (or Data Members)
           Operations (or Methods or Member Functions)
          Creation and Deletion of Instances
     Inheritance
          Single Inheritance
          Multiple and Repeated Inheritance
          Specialization of Methods
          Class Browsers
     Encapsulation
     Unified Modeling Language (UML)
     Dynamic (or Late) Binding
     Message Passing and Function Calls
     Metaclasses
     Type Checking
     Persistence
     Concurrency
     Active Values and Daemons
     OOP Summary
Objects and Agents
Frame-Based Systems
Summary: Agents, Objects, and Frames
Further Reading

Symbolic Learning
Introduction
Learning by Induction
     Overview
     Learning Viewed as a Search Problem
     Techniques for Generalization and Specialization
          Universalization
          Replacing Constants with Variables
          Using Conjunctions and Disjunctions
          Moving up or down a Hierarchy
          Chunking
Case-Based Reasoning (CBR)
     Storing Cases
          Abstraction Links and Index Links
          Instance-of Links
          Exemplar Links
          Failure Links
     Retrieving Cases
     Adapting Case Histories
          Null Adaptation
          Parameterization
          Reasoning by Analogy
          Critics
          Reinstantiation
          Dealing with Mistaken Conclusions
Summary
Further Reading

Single-Candidate Optimization Algorithms
 Optimization
The Search Space
Searching the Parameter Space
Hill-Climbing and Gradient Descent Algorithms
          Hill-Climbing
          Steepest Gradient Descent or Ascent
          Gradient-Proportional Descent or Ascent
          Conjugate Gradient Descent or Ascent
          Tabu Search
Simulated Annealing
Summary
Further Reading

Genetic Algorithms for Optimization
Introduction
The Basic GA
          Chromosomes
          Algorithm Outline
          Crossover
          Mutation
          Validity Check
Selection
          Selection Pitfalls
          Fitness-Proportionate Selection
          Fitness Scaling for Improved Selection
                    Linear Fitness Scaling
                    Sigma Scaling
                    Linear Rank Scaling
          Nonlinear Rank Scaling
          Probabilistic Nonlinear Rank Scaling
          Truncation Selection
          Transform Ranking
          Tournament Selection
          Comparison of Selection Methods
Elitism
Multiobjective Optimization
Gray Code
Building Block Hypothesis
          Schema Theorem
          Inversion
Selecting GA Parameters
Monitoring Evolution
Genetic Programming
Other Forms of Population-Based Optimization
Summary
Further Reading

Neural Networks
Introduction
Neural Network Applications
          Classification
          Nonlinear Estimation
          Clustering
          Content-Addressable Memory
Nodes and Interconnections
Single and Multilayer Perceptrons
          Network Topology
          Perceptrons as Classifiers
          Training a Perceptron
          Buffered Perceptrons
           Some Practical Considerations
Recurrent Networks
          Simple Recurrent Network (SRN)
          Hopfield Network
          MAXNET
          The Hamming Network
Unsupervised Networks
          Adaptive Resonance Theory (ART) Networks
          Kohonen Self-Organizing Networks
          Radial Basis Function Networks
Spiking Neural Networks
Summary
Further Reading

Hybrid Systems
Convergence of Techniques
Blackboard Systems for Multifaceted Problems
Parameter Setting
          Genetic–Neural Systems
          Genetic–Fuzzy Systems
Capability Enhancement
          Neuro–Fuzzy Systems
          Baldwinian and Lamarckian Inheritance in Genetic Algorithms
          Learning Classifier Systems
Clarification and Verification of Neural Network Outputs
Summary
Further Reading

Artificial Intelligence Programming Languages
A Range of Intelligent Systems Tools
Features of AI Languages
          Lists
          Other Data Types
          Programming Environments
Lisp
          Background
          Lisp Functions
          A Worked Example
Prolog
          Background
          Backtracking in Prolog
Comparison of AI Languages
Summary
Further Reading

Systems for Interpretation and Diagnosis
Introduction
Deduction and Abduction for Diagnosis
          Exhaustive Testing
          Explicit Modeling of Uncertainty
          Hypothesize-and-Test
 Depth of Knowledge
          Shallow Knowledge
          Deep Knowledge
          Shallow and Deep Knowledge
Model-Based Reasoning
          The Limitations of Rules
          Modeling Function, Structure, and State
                    Function
                    Structure
                    State
          Using the Model
          Monitoring
          Tentative Diagnosis
                    The Shotgun Approach
                    Structural Isolation
                    The Heuristic Approach
          Fault Simulation
          Fault Repair
          Using Problem Trees
          Summary of Model-Based Reasoning
Case Study: A Blackboard System for Interpreting Ultrasonic Images
          Ultrasonic Imaging
          Agents in DARBS
          Rules in DARBS
          The Stages of Image Interpretation
                    Arc Detection Using the Hough Transform
                    Gathering the Evidence
                    Defect Classification
          The Use of Neural Networks
                    Defect Classification Using a Neural Network
                    Echodynamic Classification Using a Neural Network
                     Combining the Two Applications of Neural Networks
          Rules for Verifying Neural Networks
Summary
Further Reading

Systems for Design and Selection
The Design Process
Design as a Search Problem
Computer-Aided Design
The Product Design Specification (PDS): A Telecommunications Case Study
          Background
          Alternative Views of a Network
          The Classes
                    Network
                    Link
                    Site
                     Information Stream
                    Equipment
          Summary of PDS Case Study
Conceptual Design
Constraint Propagation and Truth Maintenance
Case Study: Design of a Lightweight Beam
          Conceptual Design
          Optimization and Evaluation
          Detailed Design
Design as a Selection Exercise
          Overview
          Merit Indices
          The Polymer Selection Example
          Two-Stage Selection
          Constraint Relaxation
          A Naive Approach to Scoring
          A Better Approach to Scoring
          Case Study: Design of a Kettle
          Reducing the Search Space by Classification
Failure Mode and Effects Analysis (FMEA)
Summary
Further Reading

Systems for Planning
Introduction
Classical Planning Systems
STRIPS
          General Description
          An Example Problem
          A Simple Planning System in Prolog
Considering the Side Effects of Actions
          Maintaining a World Model
          Deductive Rules
Hierarchical Planning
          Description
          Benefits of Hierarchical Planning
          Hierarchical Planning with ABSTRIPS
Postponement of Commitment
          Partial Ordering of Plans
          The Use of Planning Variables
Job-Shop Scheduling
          The Problem
          Some Approaches to Scheduling
Constraint-Based Analysis
          Constraints and Preferences
          Formalizing the Constraints
          Identifying the Critical Sets of Operations
          Sequencing in Disjunctive Case
          Sequencing in Nondisjunctive Case
          Updating Earliest Start Times and Latest Finish Times
          Using Constraints and Preferences
Replanning and Reactive Planning
Summary
Further Reading

Systems for Control
Introduction
Low-Level Control
          Open-Loop Control
          Feedforward Control
          Feedback Control
          First- and Second-Order Models
          Algorithmic Control: The PID Controller
          Bang-Bang Control
Requirements of High-Level (Supervisory) Control
Blackboard Maintenance
Time-Constrained Reasoning
          Prioritization of Processes
          Approximation
                    Approximate Search
                    Data Approximations
                    Knowledge Approximations
                    Single and Multiple Instantiation
Fuzzy Control
The BOXES Controller
          The Conventional BOXES Algorithm
          Fuzzy BOXES
Neural Network Controllers
          Direct Association of State Variables with Action Variables
          Estimation of Critical State Variables
Statistical Process Control (SPC)
          Applications
          Collecting the Data
          Using the Data
Summary
Further Reading

The Future of Intelligent Systems
Benefits
Trends in Implementation
 Intelligent Systems and the Internet
Ubiquitous Intelligent Systems
Conclusion
References

Index

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Author(s)

Biography

Adrian Hopgood earned his BSc from the University of Bristol, PhD from the University of Oxford, and MBA from the Open University. After completing his PhD in 1984, he spent 2 years developing applied intelligent systems for Systems Designers PLC. That experience set the direction of his career toward the investigation of intelligent systems and their practical applications. After leaving Systems Designers, he spent 14 years at the Open University and remains attached as a visiting professor.

During that period, he also spent 2 years at Telstra Research Laboratories in Australia, investigating the role of intelligent systems in telecommunications. He has subsequently worked for Nottingham Trent University, De Montfort University, and Sheffield Hallam University. Despite assuming senior management positions, he has not lost his passion for intelligent systems. He has recently led the development of an open-source blackboard system, DARBS. His Website is www.adrianhopgood.com.