Knowledge-Based Systems

Book description

A knowledge-based system (KBS) is a system that uses artificial intelligence techniques in problem-solving processes to support human decision-making, learning, and action. Ideal for advanced-undergraduate and graduate students, as well as business professionals, this text is designed to help users develop an appreciation of KBS and their architecture and understand a broad variety of knowledge-based techniques for decision support and planning. It assumes basic computer science skills and a math background that includes set theory, relations, elementary probability, and introductory concepts of artificial intelligence. Each of the 12 chapters is designed to be modular, providing instructors with the flexibility to model the book to their own course needs. Exercises are incorporated throughout the text to highlight certain aspects of the material presented and to simulate thought and discussion. A comprehensive text and resource, Knowledge-Based Systems provides access to the most current information in KBS and new artificial intelligences, as well as neural networks, fuzzy logic, genetic algorithms, and soft systems.

Table of contents

  1. Book Cover
  2. Title
  3. Copyright
  4. Contents (1/3)
  5. Contents (2/3)
  6. Contents (3/3)
  7. Preface (1/2)
  8. Preface (2/2)
  9. Chapter 1 Introduction to Knowledge-Based Systems
    1. 1.1 Natural and Artificial Intelligence
    2. 1. 2 Testing the Intelligence
      1. 1.2.1 Turing Test
      2. 1.2.2 Weakness of the Turing Test
      3. 1.2.3 Chinese Room Experiment
    3. 1.3 Application Areas of Artificial Intelligence
      1. 1.3.1 Mundane Tasks
      2. 1.3.2 Formal Tasks
      3. 1.3.3 Expert Tasks
    4. 1.4 Data Pyramid and Computer-Based Systems
      1. 1.4.1 Data
      2. 1.4.2 Information
      3. 1.4.3 Knowledge
      4. 1.4.4 Wisdom and Intelligence
      5. 1.4.5 Skills Versus Knowledge
    5. 1.5 Knowledge-Based Systems
    6. 1.6 Objectives of KBS
    7. 1.7 Components of KBS
    8. 1.8 Categories of KBS
      1. 1.8.1 Expert Systems
      2. 1.8.2 Database Management Systems in Conjunction with an Intelligent User Interface
      3. 1.8.3 Linked Systems
      4. 1.8.4 CASE -Based Systems
      5. 1.8.5 Intelligent Tutoring Systems
    9. 1.9 Difficulties with the KBS
      1. 1.9.1 Completeness of Knowledge Base
      2. 1.9.2 Characteristics of Knowledge
      3. 1.9.3 Large Size of Knowledge Base
      4. 1.9.4 Acquisition of Knowledge
      5. 1.9.5 Slow Learning and Execution
    10. 1.10 Warm-up Questions, Exercises, and Projects
  10. Chapter 2 Knowledge-Based Systems Architecture
    1. 2.1 Source of the Knowledge
    2. 2.2 Types of Knowledge
      1. 2.2.1 Commonsense and Informed Commonsense Knowledge
      2. 2.2.2 Heuristic Knowledge
      3. 2.2.3 Domain Knowledge
      4. 2.2.4 Metaknowledge
      5. 2.2.5 Classifying Knowledge According to Its Use
      6. 2.2.6 Classifying Knowledge According to Its Nature
    3. 2.3 Desirable Characteristics of Knowledge
    4. 2.4 Components of Knowledge
      1. 2.4.1 Facts
      2. 2.4.2 Rules
      3. 2.4.3 Heuristics
    5. 2.5 Basic Structure of Knowledge-Based Systems
    6. 2.6 Knowledge Base
    7. 2.7 Inference Engine
      1. 2.7.1 Modus Ponens
      2. 2.7.2 Modus Tollens
      3. 2.7.3 Forward Chaining
      4. 2.7.4 Backward Chaining
      5. 2.7.5 Forward Versus Backward Chaining
      6. 2.7.6 Conflict Resolution Strategies for Rule-Based Systems
    8. 2.8 Self-Learning
    9. 2.9 Reasoning
    10. 2.10 Explanation
    11. 2.11 Applications
      1. 2.11.1 Advisory Systems
      2. 2.11.2 Health Care and Medical Diagnosis Systems
      3. 2.11.3 Tutoring Systems
      4. 2.11.4 Control and Monitoring
      5. 2.11.5 Prediction
      6. 2.11.6 Planning
      7. 2.11.7 Searching Larger Databases and Data Warehouses
      8. 2.11.8 Knowledge-Based Grid and Semantic Web
    12. 2.12 Knowledge-Based Shell
    13. 2.13 Advantages of Knowledge-Based Systems
      1. 2.13.1 Permanent Documentation of Knowledge
      2. 2.13.2 Cheaper Solution and Easy Availability of Knowledge
      3. 2.13.3 Dual Advantages of Effectiveness and Efficiency
      4. 2.13.4 Consistency and Reliability
      5. 2.13.5 Justification for Better Understanding
      6. 2.13.6 Self-Learning and Ease of Updates
    14. 2.14 Limitations of Knowledge-Based Systems
      1. 2.14.1 Partial Self-Learning
      2. 2.14.2 Creativity and Innovation
      3. 2.14.3 Weak Support of Methods and Heuristics
      4. 2.14.4 Development Methodology
      5. 2.14.5 Knowledge Acquisition
      6. 2.14.6 Structured Knowledge Representation and Ontology Mapping
      7. 2.14.7 Development of Testing and Certifying Strategies and Standards for Knowledge-Based Systems
    15. 2.15 Warm-up Questions, Exercises, and Projects
  11. Chapter 3 Developing Knowledge-Based Systems
    1. 3.1 Nature of Knowledge-Based Systems
    2. 3.2 Difficulties in KBS Development
      1. 3.2.1 High Cost and Effort
      2. 3.2.2 Dealing with Experts
      3. 3.2.3 The Nature of the Knowledge
      4. 3.2.4 The High Level of Risk
    3. 3.3 Knowledge-Based Systems Development Model
    4. 3.4 Knowledge Acquisition
      1. 3.4.1 Knowledge Engineer
      2. 3.4.2 Domain Experts
      3. 3.4.3 Knowledge Elicitation
      4. 3.4.4 Steps of Knowledge Acquisition
    5. 3.5 Existing Techniques for Knowledge Acquisition
      1. 3.5.1 Reviewing the Literature
      2. 3.5.2 Interview and Protocol Analysis
      3. 3.5.3 Surveys and Questionnaires
      4. 3.5.4 Observation
      5. 3.5.5 Diagram-Based Techniques
      6. 3.5.6 Generating Prototypes
      7. 3.5.7 Concept Sorting
    6. 3.6 Developing Relationships with Experts
    7. 3.7 Sharing Knowledge
      1. 3.7.1 Problem Solving
      2. 3.7.2 Talking and Storytelling
      3. 3.7.3 Supervisory Style
    8. 3.8 Dealing with Multiple Experts
      1. 3.8.1 Handling Individual Experts
      2. 3.8.2 Handling Experts in Hierarchical Fashion
      3. 3.8.3 Small-Group Approach
    9. 3.9 Issues with Knowledge Acquisition
    10. 3.10 Updating Knowledge
      1. 3.10.1 Self-Updates
      2. 3.10.2 Manual Updates by Knowledge Engineer
      3. 3.10.3 Manual Updates by Experts
    11. 3.11 Knowledge Representation
    12. 3.12 Factual Knowledge
      1. 3.12.1 Constants
      2. 3.12.2 Variables
      3. 3.12.3 Functions
      4. 3.12.4 Predicates
      5. 3.12.5 Well-Formed Formulas
      6. 3.12.6 First-Order Logic
    13. 3.13 Representing Procedural Knowledge
      1. 3.13.1 Production Rules
      2. 3.13.2 Semantic Networks
      3. 3.13.3 Frames
      4. 3.13.4 Scripts
      5. 3.13.5 Hybrid Structures
      6. 3.13.6 Semantic Web Structures
    14. 3.14 Users of Knowledge-Based Systems
    15. 3.15 Knowledge-Based System Tools
      1. 3.15.1 C Language Integrated Production System (CLIPS)
      2. 3.15.2 Java Expert System Shell (JESS)
    16. 3.16 Warm-up Questions, Exercises, and Projects
  12. Chapter 4 Knowledge Management
    1. 4.1 Introduction to Knowledge Management
    2. 4.2 Perspectives of Knowledge Management
      1. 4.2.1 Technocentric
      2. 4.2.2 Organizational
      3. 4.2.3 Ecological
    3. 4.3 What Drives Knowledge Management?
      1. 4.3.1 Size and Dispersion of an Organization
      2. 4.3.2 Reducing Risk and Uncertainty
      3. 4.3.3 Improving the Quality of Decisions
      4. 4.3.4 Improving Customer Relationships
      5. 4.3.5 Technocentric Support
      6. 4.3.6 Intellectual Asset Management and Prevention of Knowledge Loss
      7. 4.3.7 Future Use of Knowledge
      8. 4.3.8 Increase Market Value and Enhance an Organization’s Brand Image
      9. 4.3.9 Shorter Product Cycles
      10. 4.3.10 Restricted Access and Added Security
    4. 4.4 Typical Evolution of Knowledge Management within an Organization
      1. 4.4.1 Ad-hoc Knowledge
      2. 4.4.2 Sophisticated Knowledge Management
      3. 4.4.3 Embedded Knowledge Management
      4. 4.4.4 Integrated Knowledge Management
    5. 4.5 Elements of Knowledge Management
      1. 4.5.1 People and Skills
      2. 4.5.2 Procedures
      3. 4.5.3 Strategy and Policy
      4. 4.5.4 Technology
    6. 4.6 The Knowledge Management Process
      1. 4.6.1 Knowledge Discovery and Innovation
      2. 4.6.2 Knowledge Documentation
      3. 4.6.3 Knowledge Use
      4. 4.6.4 Knowledge Sharing Through Pull and Push Technologies
    7. 4.7 Knowledge Management Tools and Technologies
      1. 4.7.1 Tools for Discovering Knowledge
      2. 4.7.2 Tools for Documenting Knowledge
      3. 4.7.3 Tools for Sharing and Using Knowledge
      4. 4.7.4 Technologies for Knowledge Management
    8. 4.8 Knowledge Management Measures
    9. 4.9 Knowledge Management Organization
    10. 4.10 Knowledge Management Roles and Responsibilities
      1. 4.10.1 Chief Knowledge Officer (CKO)
      2. 4.10.2 Knowledge Engineer (KE)
      3. 4.10.3 Knowledge Facilitator (KF)
      4. 4.10.4 Knowledge Worker (KW)
      5. 4.10.5 Knowledge Consultant (KC)
    11. 4.11 Knowledge Management Models
      1. 4.11.1 Transaction Model
      2. 4.11.2 Cognitive Model
      3. 4.11.3 Network Model
      4. 4.11.4 Community Model
    12. 4.12 Models for Categorizing Knowledge
      1. 4.12.1 Knowledge Spiral Model
      2. 4.12.2 Knowledge Management Model
      3. 4.12.3 Knowledge Category Model
    13. 4.13 Models for Intellectual Capital Management
    14. 4.14 Socially Constructed Knowledge Management Models
    15. 4.15 Techniques to Model Knowledge
      1. 4.15.1 CommonKADS
      2. 4.15.2 Protégé 2000
    16. 4.16 K-commerce
    17. 4.17 Benefits of Knowledge Management
      1. 4.17.1 Knowledge-Related Benefits
      2. 4.17.2 Organizational and Administrative Benefits
      3. 4.17.3 Individual Benefits
    18. 4.18 Challenges of Knowledge Management
    19. 4.19 Warm-up Questions, Exercises, and Projects
  13. Chapter 5 Fuzzy Logic
    1. 5.1 Introduction
    2. 5.2 Fuzzy Logic and Bivalued Logic
      1. 5.2.1 Fuzzy Versus Probability
    3. 5.3 Fuzzy Logic and Fuzzy Sets
    4. 5.4 Membership Functions
      1. 5.4.1 Fuzzification
      2. 5.4.2 Defuzzification
    5. 5.5 Operations on Fuzzy Sets
      1. 5.5.1 Intersection of Fuzzy Sets
      2. 5.5.2 Union of Fuzzy Sets
      3. 5.5.3 Complements of Fuzzy Sets
      4. 5.5.4 Equality of Fuzzy Sets
    6. 5.6 Types of Fuzzy Functions
      1. 5.6.1 Quasi-Fuzzy Membership Functions
      2. 5.6.2 Triangular Fuzzy Membership Functions
      3. 5.6.3 Trapezoidal Fuzzy Membership Function
    7. 5.7 Linguistic Variables
      1. 5.7.1 Linguistic Hedges
    8. 5.8 Fuzzy Relationships
    9. 5.9 Fuzzy Propositions
      1. 5.9.1 Fuzzy Connectives
    10. 5.10 Fuzzy Inference
    11. 5.11 Fuzzy Rules
    12. 5.12 Fuzzy Control System
    13. 5.13 Fuzzy Rule-Based System
      1. 5.13.1 Models of Fuzzy Rule-Based Systems
    14. 5.14 Type-1 and Type-2 Fuzzy Rule-Based Systems
      1. 5.14.1 T2 FS Membership Functions
    15. 5.15 Modeling Fuzzy Systems
    16. 5.16 Limitations of Fuzzy Systems
    17. 5.17 Applications and Research Trends in Fuzzy Logic-Based Systems
    18. 5.18 Warm-up Questions, Exercises, and Projects
  14. Chapter 6 Agent–Based Systems
    1. 6.1 Introduction
    2. 6.2 What is an Agent?
    3. 6.3 Characteristics of Agents
    4. 6.4 Advantages of Agent Technology
    5. 6.5 Agent Typologies
      1. 6.5.1 Collaborative Agent
      2. 6.5.2 Interface Agent
      3. 6.5.3 Mobile Agent
      4. 6.5.4 Information Agent
      5. 6.5.5 Hybrid Agent
    6. 6.6 Agent Communication Languages
    7. 6.7 Standard Communicative Actions
    8. 6.8 Agents and Objects
    9. 6.9 Agents, AI, and Intelligent Agents
    10. 6.10 Multiagent Systems
      1. 6.10.1 Layered Architecture of a Generic Multiagent System
    11. 6.11 Knowledge Engineering-Based Methodologies
      1. 6.11.1 MAS-CommonKADS
      2. 6.11.2 Desire
    12. 6.12 Case Study
      1. 6.12.1 Partial Discharge Diagnosis within a GIS
      2. 6.12.2 Intelligent Agents for GIS Monitoring
    13. 6.13 Directions for Further Research
    14. 6.14 Warm-up Questions, Exercises, and Projects
  15. Chapter 7 Connectionist Models
    1. 7.1 Introduction
      1. 7.1.1 Advantages and Disadvantages of Neural Networks
      2. 7.1.2 Comparing Artificial Neural Networks with the von Neumann Model
    2. 7.2 Biological Neurons
    3. 7.3 Artificial Neurons
    4. 7.4 Neural Network Architectures
      1. 7.4.1 Hopfield Model
      2. 7.4.2 Learning in a Hopfield Network Through Parallel Relaxation
      3. 7.4.3 Perceptrons
      4. 7.4.4 Perceptron Learning Rule
      5. 7.4.5 Fixed-Increment Perceptron Learning Algorithms
      6. 7.4.6 Multilayer Perceptrons
      7. 7.4.7 Back-Propagation Algorithms
    5. 7.5 Learning Paradigms
    6. 7.6 Other Neural Network Models
      1. 7.6.1 Kohonen Maps
      2. 7.6.2 Probabilistic Neural Networks
    7. 7.7 Integrating Neural Networks and Knowledge-Based Systems
    8. 7.8 Applications for Neural Networks
      1. 7.8.1 Applications for the Back-Propagation Model
    9. 7.9 Warm-up Questions, Exercises, and Projects
  16. Chapter 8 Genetic Algorithms
    1. 8.1 Introduction
    2. 8.2 Basic Terminology
    3. 8.3 Genetic Algorithms
    4. 8.4 Genetic Cycles
    5. 8.5 Basic Operators of a Genetic Algorithm
      1. 8.5.1 Mutation
      2. 8.5.2 Crossover
      3. 8.5.3 Selection
    6. 8.6 Function Optimization
      1. 8.6.1 Stopping Criteria
    7. 8.7 Schema
      1. 8.7.1 Schema Defined
      2. 8.7.2 Instance, Defined Bits, and Order of Schema
      3. 8.7.3 The Importance of Schema Results
    8. 8.8 Ordering Problems and Edge Recombination
      1. 8.8.1 Traveling Salesperson Problem
      2. 8.8.2 Solutions to Prevent Production of Invalid Offspring
      3. 8.8.3 Edge Recombination Technique
    9. 8.9 Island-Based Genetic Algorithms
    10. 8.10 Problem Solving Using Genetic Algorithms
    11. 8.11 Bayesian Networks and Genetic Algorithms
    12. 8.12 Applications and Research Trends in GA
    13. 8.13 Warm-up Questions, Exercises, and Projects
  17. Chapter 9 Soft Computing Systems
    1. 9.1 Introduction to Soft Computing
    2. 9.2 Constituents of Soft Computing
    3. 9.3 Characteristics of Soft Computing
      1. 9.3.1 Simulation of Human Expertise
      2. 9.3.2 Innovative Techniques
      3. 9.3.3 Natural Evolution
      4. 9.3.4 Model-Free Learning
      5. 9.3.5 Goal-Driven
      6. 9.3.6 Extensive Numerical Computations
      7. 9.3.7 Dealing with Partial and Incomplete Information
      8. 9.3.8 Fault Tolerance
    4. 9.4 Neuro-Fuzzy Systems
      1. 9.4.1 Fuzzy Neural Networks
      2. 9.4.2 Cooperative Neuro-Fuzzy Model
      3. 9.4.3 Concurrent Neuro-Fuzzy Model
      4. 9.4.4 Hybrid Neuro-Fuzzy Model
    5. 9.5 Genetic-Fuzzy Systems
      1. 9.5.1 Genetic Algorithms Controlled by Fuzzy Logic
      2. 9.5.2 Fuzzy Evolutionary Systems
      3. 9.5.3 Evolving Knowledge Bases and Rule Sets
    6. 9.6 Neuro-Genetic Systems
      1. 9.6.1 Neural Network Weight Training
      2. 9.6.2 Evolving Neural Nets
    7. 9.7 Genetic-Fuzzy-Neural Networks
    8. 9.8 Chaos Theory
      1. 9.8.1 Basic Constructs
      2. 9.8.2 Hybridization
    9. 9.9 Rough Set Theory
      1. 9.9.1 Pawlak’s Information System
      2. 9.9.2 Rough Sets
      3. 9.9.3 Rough Logic
      4. 9.9.4 Rough Models
      5. 9.9.5 Rough-Set–Based Systems
    10. 9.10 Applications of Soft Computing
    11. 9.11 Warm-up Questions, Exercises, and Projects
  18. Chapter 10 Knowledge–Based Multiagent System Accessing Distributed Database Grid: An E–Learning Solution
    1. 10.1 Introduction and Background
      1. 10.1.1 E-learning Defined
      2. 10.1.2 Major Components of E-learning
      3. 10.1.3 Objectives of E-learning
      4. 10.1.4 Advantages of E-learning
    2. 10.2 Existing E-learning Solutions: Work Done so Far
    3. 10.3 Requirements for an Ideal E-learning Solution
      1. 10.3.1 Quality Parameters for an Ideal E-learning Solution
    4. 10.4 Toward a Knowledge-Based Multiagent Approach
      1. 10.4.1 Objectives of a Knowledge-Based Multiagent E-Learning Solution
      2. 10.4.2 Introduction to Multiagent Systems
      3. 10.4.3 Advantages of a Knowledge-Based Multiagent Approach for E-learning
    5. 10.5 System Architecture and Methodology
      1. 10.5.1 System Agents
      2. 10.5.2 Interaction Between Agents
      3. 10.5.3 Middleware Services
    6. 10.6 Knowledge Representation and System Output
    7. 10.7 Results of the Experiment
      1. 10.7.1 Advantages Achieved
    8. 10.8 Conclusion
  19. Chapter 11 Knowledge–Intensive Learning: Diet Menu Planner
    1. 11.1 Introduction
    2. 11.2 Case Retrieval
      1. 11.2.1 The Identify Features
      2. 11.2.2 Matching
    3. 11.3 Case Reuse
    4. 11.4 Case Revision
    5. 11.5 Case Retention
    6. 11.6 Organization of Cases in Memory
    7. 11.7 DietMaster
      1. 11.7.1 General Menu-Planning Process for Diabetic Patients
      2. 11.7.2 The DietMaster Architecture
    8. 11.8 Knowledge Model
    9. 11.9 Representation of Different Knowledge Types
      1. 11.9.1 Case Structure
      2. 11.9.2 General Knowledge
      3. 11.9.3 Rules
      4. 11.9.4 Procedures
    10. 11.10 Problem Solving in DietMaster
    11. 11.11 Integrated Reasoning in DietMaster
    12. 11.12 Problem Solving and Reasoning Algorithm
    13. 11.13 The Learning Process
      1. 11.13.1 The Learning Algorithm
    14. 11.14 Feedback on Diet Plan
    15. 11.15 Conclusion
  20. Chapter 12 Natural Language Interface: Question Answering System
    1. 12.1 Introduction
      1. 12.1.1 Open-Domain Question Answering
      2. 12.1.2 Closed-Domain Question Answering
    2. 12.2 Natural Language Interface to Structured Data
    3. 12.3 Natural Language Interface to Unstructured Data
    4. 12.4 Different Approaches to Language
      1. 12.4.1 Symbolic (Rule-Based) Approach
      2. 12.4.2 Empirical (Corpus-Based) Approach
      3. 12.4.3 Connectionist Approach (Usingsing a Neural Network)
    5. 12.5 Semantic-Level Problems
    6. 12.6 Shallow Parsing
      1. 12.6.1 Semantic Symmetry
      2. 12.6.2 Sentence Patterns and Semantic Symmetry
      3. 12.6.3 An Algorithm
    7. 12.7 Ambiguous Modification
    8. 12.8 Conclusion
  21. Index

Product information

  • Title: Knowledge-Based Systems
  • Author(s): Akerkar
  • Release date: August 2010
  • Publisher(s): Jones & Bartlett Learning
  • ISBN: 9781449612948