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1 Knowledge-Based - Systems ; Rajendra Arvind Akerkar Chairman, Technomathematics Research Foundation and Senior Researcher, Western Norway Research institute Priti Srinivas Sajja Sardar Patel University JONES A N D BARTLETT PUBLISHERS Sudbury, Massachusetts BOSTON TORONTO LONDON SINGAPORE

2 Contents Preface xvii Chapter 1 Introduction to Knowledge-Based Systems Natural and Artificial Intelligence Testing the Intelligence Turing Test Weakness of the Turing Test Chinese Room Experiment Application Areas of Artificial Intelligence Mundane Tasks Formal Tasks Expert Tasks Data Pyramid and Computer-Based Systems Data Information Knowledge Wisdom and Intelligence Skills Versus Knowledge Knowledge-Based Systems Objectives of KBS Components of KBS Categories of KBS Expert Systems Database Management Systems in Conjunction with an Intelligent User Interface Linked Systems CASE-Based Systems Intelligent Tutoring Systems Difficulties with the KBS Completeness of Knowledge Base Characteristics of Knowledge 23

3 vi CONTENTS Large Size of Knowledge Base Acquisition of Knowledge Slow Learning and Execution Warm-Up Questions, Exercises, and Projects 25 Chapter 2 Knowledge-Based Systems Architecture Source of the Knowledge Types of Knowledge Commonsense and Informed Commonsense Knowledge Heuristic Knowledge Domain Knowledge Metaknowledge Classifying Knowledge According to Its Use Classifying Knowledge According to Its Nature Desirable Characteristics of Knowledge Components of Knowledge Facts Rules Heuristics Basic Structure of Knowledge-Based Systems Knowledge Base Inference Engine Modus Ponens Modus Tollens Forward Chaining Backward Chaining ForwardVersus Backward Chaining Conflict Resolution Strategies for Rule-Based Systems Self-Learning Reasoning Explanation Applications Advisory Systems Health Care and Medical Diagnosis Systems Tutoring Systems Control and Monitoring Prediction Planning Searching Larger Databases and Data Warehouses Knowledge-Based Grid and Semantic Web Knowledge-Based SheU Advantages of Knowledge-Based Systems Permanent Documentation of Knowledge Cheaper Solution and Easy Availability of Knowledge 46

4 CONTENTS vii Dual Advantages of Effectiveness and Efficiency Consistency and Reliability Justification for Better Understanding Self-Learning and Ease of Updates Limitations of Knowledge-Based Systems Partial Self-Learning Creativity and Innovation Weak Support of Methods and Heuristics Development Methodology Knowledge Acquisition Structured Knowledge Representation and Ontology Mapping Development of Testing and Certifying Strategies and Standards for Knowledge-Based Systems Warm-Up Questions, Exercises, and Projects 51 Chapter 3 Developing Knowledge-Based Systems Nature of Knowledge-Based Systems Difficulties in KBS Development High Cost and Effort Dealing with Experts The Nature of the Knowledge The High Level of Risk Knowledge-Based Systems Development Model Knowledge Acquisition Knowledge Engineer Domain Experts Knowledge Elicitation Steps of Knowledge Acquisition Existing Techniques for Knowledge Acquisition Reviewing the Literature Interview and Protocol Analysis Surveys and Questionnaires Observation Diagram-Based Techniques Generating Prototypes Concept Sorting Developing Relationships with Experts Sharing Knowledge Problem Solving Talking and Storytelling Supervisory Style Dealing with Multiple Experts Handling Individual Experts 66

5 viii CONTENTS Handling Experts in Hierarchical Fashion Small-Group Approach Issues with Knowledge Acquisition Updating Knowledge Self-Updates Manual Updates by Knowledge Engineer Manual Updates by Experts Knowledge Representation Factual Knowledge Constants Variables Functions Predicates Well-Formed Formulas First-Order Logic Representing Procedural Knowledge Production Rules Semantic Networks Frames Scripts Hybrid Structures Semantic Web Structures Users of Knowledge-Based Systems Knowledge-Based System Tools С Language Integrated Production System (CLIPS) Java Expert System Shell (JESS) Warm-Up Questions, Exercises, and Projects 90 Chapter 4 Knowledge Management Introduction to Knowledge Management Perspectives of Knowledge Management Technocentric Organizational Ecological What Drives Knowledge Management? Size and Dispersion of an Organization Reducing Risk and Uncertainty Improving the Quality of Decisions Improving Customer Relationships Technocentric Support Intellectual Asset Management and Prevention of Knowledge Loss Future Use of Knowledge 99

6 CONTENTS ix Increase Market Value and Enhance an Organization's Brand Image Shorter Product Cycles Restricted Access and Added Security Typical Evolution of Knowledge Management within an Organization Ad-hoc Knowledge Sophisticated Knowledge Management Embedded Knowledge Management Integrated Knowledge Management Elements of Knowledge Management People and Skills Procedures Strategy and Policy Technology The Knowledge Management Process Knowledge Discovery and Innovation Knowledge Documentation Knowledge Use Knowledge Sharing Through Pull and Push Technologies Knowledge Management Tools and Technologies Tools for Discovering Knowledge Tools for Documenting Knowledge Tools for Sharing and Using Knowledge Technologies for Knowledge Management Knowledge Management Measures Knowledge Management Organization Knowledge Management Roles and Responsibilities Chief Knowledge Officer (CKO) Knowledge Engineer (KE) Knowledge Facilitator (KF) Knowledge Worker (KW) Knowledge Consultant (КС) Knowledge Management Models Transaction Model Cognitive Model Network Model Community Model Models for Categorizing Knowledge Knowledge Spiral Model Knowledge Management Model Knowledge Category Model Models for Intellectual Capital Management Socially Constructed Knowledge Management Models 119

7 x CONTENTS 4.15 Techniques to Model Knowledge CommonKADS Protege K-Commerce Benefits of Knowledge Management Knowledge-Related Benefits Organizational and Administrative Benefits Individual Benefits Challenges of Knowledge Management Warm-Up Questions, Exercises, and Projects 125 Chapter 5 Fuzzy Logic Introduction Fuzzy Logic and Bivalued Logic Fuzzy Versus Probability Fuzzy Logic and Fuzzy Sets Membership Functions Fuzzification Defuzzification Operations on Fuzzy Sets Intersection of Fuzzy Sets Union of Fuzzy Sets Complements of Fuzzy Sets Equality of Fuzzy Sets Types of Fuzzy Functions Quasi-Fuzzy Membership Functions Triangular Fuzzy Membership Functions Trapezoidal Fuzzy Membership Function Linguistic Variables Linguistic Hedges Fuzzy Relationships Fuzzy Propositions Fuzzy Connectives Fuzzy Inference Fuzzy Rules Fuzzy Control System Fuzzy Rule-Based System Models of Fuzzy Rule-Based Systems Type-1 and Type-2 Fuzzy Rule-Based Systems T2 FS Membership Functions Modeling Fuzzy Systems Limitations of Fuzzy Systems 150

8 CONTENTS xi 5.17 Applications and Research Trends in Fuzzy Logic-Based Systems Warm-Up Questions, Exercises, and Projects 152 Chapter 6 Agent-Based Systems Introduction What Is an Agent? Characteristics of Agents Advantages of Agent Technology Agent Typologies Collaborative Agent Interface Agent Mobile Agent Information Agent Hybrid Agent Agent Communication Languages Standard Communicative Actions Agents and Objects Agents, AI, and Intelligent Agents Multiagent Systems Layered Architecture of a Generic Multiagent System Knowledge Engineering-Based Methodologies MAS-CommonKADS DESIRE Case Study Partial Discharge Diagnosis Within a GIS Intelligent Agents for GIS Monitoring Directions for Further Research Warm-Up Questions, Exercises, and Projects 184 Chapter 7 Connectionist Models Introduction Advantages and Disadvantages of Neural Networks Comparing Artificial Neural Networks with the von Neumann Model Biological Neurons Artificial Neurons Neural Network Architectures Hopfield Model Learning in a Hopfield Network Through Parallel Relaxation Perceptrons Perceptron Learning Rule Fixed-Increment Perceptron Learning Algorithms Multilayer Perceptrons Back-Propagation Algorithms 201

9 xii CONTENTS 7.5 Learning Paradigms Other Neural Network Models KohonenMaps Probabilistic Neural Networks Integrating Neural Networks and Knowledge-Based Systems Applications for Neural Networks Applications for the Back-Propagation Model Warm-Up Questions, Exercises, and Projects 212 Chapter 8 Genetic Algorithms Introduction Basic Terminology Genetic Algorithms Genetic Cycles Basic Operators of a Genetic Algorithm Mutation Crossover Selection Function Optimization Stopping Criteria Schema Schema Defined Instance, Defined Bits, and Order of Schema The Importance of Schema Results Ordering Problems and Edge Recombination Traveling Salesperson Problem Solutions to Prevent Production of Invalid Offspring Edge Recombination Technique Island-Based Genetic Algorithms Problem Solving Using Genetic Algorithms Bayesian Networks and Genetic Algorithms Applications and Research Trends in GA Warm-Up Questions, Exercises, and Projects 236 Chapter 9 Soft Computing Systems Introduction to Soft Computing Constituents of Soft Computing Characteristics of Soft Computing Simulation of Human Expertise Innovative Techniques Natural Evolution Model-Free Learning 243

10 CONTENTS xiii Goal-Driven Extensive Numerical Computations Dealing with Partial and Incomplete Information Fault Intolerance Neuro-Fuzzy Systems Fuzzy Neural Networks Cooperative Neuro-Fuzzy Model Concurrent Neuro-Fuzzy Model Hybrid Neuro-Fuzzy Model Genetic-Fuzzy Systems Genetic Algorithms Controlled by Fuzzy Logic Fuzzy Evolutionary Systems Evolving Knowledge Bases and Rule Sets Neuro-Genetic Systems Neural Network Weight Training Evolving Neural Nets Genetic-Fuzzy-Neural Networks Chaos Theory Basic Constructs Hybridization Rough Set Theory Pawlak's Information System Rough Sets Rough Logic Rough Models Rough-Set-Based Systems Applications of Soft Computing Warm-Up Questions, Exercises, and Projects 272 Chapter 10 Knowledge-Based Multiagent System Accessing Distributed Database Grid: An E-Learning Solution Introduction and Background E-learning Defined Major Components of E-learning Objectives of E-learning Advantages of E-learning Existing E-learning Solutions: Work Done So Far Requirements for an Ideal E-learning Solution Quality Parameters for an Ideal E-learning Solution Toward a Knowledge-Based Multiagent Approach Objectives of a Knowledge-Based Multiagent E-learning Solution Introduction to Multiagent Systems Advantages of a Knowledge-Based Multiagent Approach for E-learning 285

11 xiv CONTENTS 10.5 System Architecture and Methodology System Agents Interaction Between Agents Middleware Services Knowledge Representation and System Output Results of the Experiment Advantages Achieved Conclusion 292 Chapter 11 Knowledge-Intensive Learning: Diet Menu Planner Introduction Case Retrieval The Identify Features Matching Case Reuse Case Revision Case Retention Organization of Cases in Memory DietMaster General Menu-Planning Process for Diabetic Patients The DietMaster Architecture Knowledge Model Representation of Different Knowledge Types Case Structure General Knowledge Rules Procedures Problem Solving in DietMaster Integrated Reasoning in DietMaster Problem Solving and Reasoning Algorithm The Learning Process The Learning Algorithm Feedback on Diet Plan Conclusion 322 Chapter 12 Natural Language Interface: Question Answering System Introduction Open-Domain Question Answering Closed-Domain Question Answering Natural Language Interface to Structured Data Natural Language Interface to Unstructured Data Different Approaches to Language 334

12 Symbolic (Rule-Based) Approach Empirical (Corpus-Based) Approach Connectionist Approach (Using a Neural Network) 336 Semantic-Level Problems 336 Shallow Parsing Semantic Symmetry Sentence Patterns and Semantic Symmetry An Algorithm 340 Ambiguous Modification 341 Conclusion 344 Index 347

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