book

Knowledge-Based Systems

by Rajendra Akerkar, Priti Sajja

August 2009

Intermediate to advanced

354 pages

10h 10m

English

Jones & Bartlett Learning

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Book Cover
Title
Copyright
Contents (1/3)
Contents (2/3)
Contents (3/3)
Preface (1/2)
Preface (2/2)
Chapter 1 Introduction to Knowledge-Based Systems
1.1 Natural and Artificial Intelligence
1. 2 Testing the Intelligence
1.2.1 Turing Test

1.2.2 Weakness of the Turing Test
1.2.3 Chinese Room Experiment
1.3 Application Areas of Artificial Intelligence
1.3.1 Mundane Tasks
1.3.2 Formal Tasks
1.3.3 Expert Tasks
1.4 Data Pyramid and Computer-Based Systems
1.4.1 Data
1.4.2 Information
1.4.3 Knowledge
1.4.4 Wisdom and Intelligence
1.4.5 Skills Versus Knowledge
1.5 Knowledge-Based Systems
1.6 Objectives of KBS
1.7 Components of KBS
1.8 Categories of KBS
1.8.1 Expert Systems
1.8.2 Database Management Systems in Conjunction with an Intelligent User Interface
1.8.3 Linked Systems
1.8.4 CASE -Based Systems1.8.5 Intelligent Tutoring Systems
1.9 Difficulties with the KBS
1.9.1 Completeness of Knowledge Base1.9.2 Characteristics of Knowledge1.9.3 Large Size of Knowledge Base
1.9.4 Acquisition of Knowledge
1.9.5 Slow Learning and Execution
1.10 Warm-up Questions, Exercises, and Projects
Chapter 2 Knowledge-Based Systems Architecture
2.1 Source of the Knowledge
2.2 Types of Knowledge
2.2.1 Commonsense and Informed Commonsense Knowledge2.2.2 Heuristic Knowledge2.2.3 Domain Knowledge
2.2.4 Metaknowledge
2.2.5 Classifying Knowledge According to Its Use2.2.6 Classifying Knowledge According to Its Nature
2.3 Desirable Characteristics of Knowledge
2.4 Components of Knowledge2.4.1 Facts
2.4.2 Rules
2.4.3 Heuristics2.5 Basic Structure of Knowledge-Based Systems
2.6 Knowledge Base
2.7 Inference Engine
2.7.1 Modus Ponens
2.7.2 Modus Tollens
2.7.3 Forward Chaining
2.7.4 Backward Chaining
2.7.5 Forward Versus Backward Chaining
2.7.6 Conflict Resolution Strategies for Rule-Based Systems
2.8 Self-Learning2.9 Reasoning
2.10 Explanation
2.11 Applications2.11.1 Advisory Systems
2.11.2 Health Care and Medical Diagnosis Systems
2.11.3 Tutoring Systems2.11.4 Control and Monitoring2.11.5 Prediction
2.11.6 Planning
2.11.7 Searching Larger Databases and Data Warehouses2.11.8 Knowledge-Based Grid and Semantic Web2.12 Knowledge-Based Shell
2.13 Advantages of Knowledge-Based Systems
2.13.1 Permanent Documentation of Knowledge2.13.2 Cheaper Solution and Easy Availability of Knowledge
2.13.3 Dual Advantages of Effectiveness and Efficiency
2.13.4 Consistency and Reliability2.13.5 Justification for Better Understanding2.13.6 Self-Learning and Ease of Updates
2.14 Limitations of Knowledge-Based Systems
2.14.1 Partial Self-Learning2.14.2 Creativity and Innovation2.14.3 Weak Support of Methods and Heuristics
2.14.4 Development Methodology
2.14.5 Knowledge Acquisition
2.14.6 Structured Knowledge Representation and Ontology Mapping
2.14.7 Development of Testing and Certifying Strategies and Standards for Knowledge-Based Systems
2.15 Warm-up Questions, Exercises, and Projects
Chapter 3 Developing Knowledge-Based Systems
3.1 Nature of Knowledge-Based Systems3.2 Difficulties in KBS Development
3.2.1 High Cost and Effort
3.2.2 Dealing with Experts3.2.3 The Nature of the Knowledge3.2.4 The High Level of Risk
3.3 Knowledge-Based Systems Development Model
3.4 Knowledge Acquisition
3.4.1 Knowledge Engineer3.4.2 Domain Experts3.4.3 Knowledge Elicitation3.4.4 Steps of Knowledge Acquisition
3.5 Existing Techniques for Knowledge Acquisition
3.5.1 Reviewing the Literature3.5.2 Interview and Protocol Analysis
3.5.3 Surveys and Questionnaires
3.5.4 Observation3.5.5 Diagram-Based Techniques3.5.6 Generating Prototypes3.5.7 Concept Sorting
3.6 Developing Relationships with Experts
3.7 Sharing Knowledge
3.7.1 Problem Solving
3.7.2 Talking and Storytelling3.7.3 Supervisory Style3.8 Dealing with Multiple Experts
3.8.1 Handling Individual Experts
3.8.2 Handling Experts in Hierarchical Fashion3.8.3 Small-Group Approach
3.9 Issues with Knowledge Acquisition
3.10 Updating Knowledge3.10.1 Self-Updates3.10.2 Manual Updates by Knowledge Engineer
3.10.3 Manual Updates by Experts
3.11 Knowledge Representation
3.12 Factual Knowledge
3.12.1 Constants3.12.2 Variables3.12.3 Functions
3.12.4 Predicates
3.12.5 Well-Formed Formulas3.12.6 First-Order Logic
3.13 Representing Procedural Knowledge
3.13.1 Production Rules
3.13.2 Semantic Networks
3.13.3 Frames
3.13.4 Scripts
3.13.5 Hybrid Structures
3.13.6 Semantic Web Structures
3.14 Users of Knowledge-Based Systems
3.15 Knowledge-Based System Tools
3.15.1 C Language Integrated Production System (CLIPS)
3.15.2 Java Expert System Shell (JESS)
3.16 Warm-up Questions, Exercises, and Projects
Chapter 4 Knowledge Management
4.1 Introduction to Knowledge Management
4.2 Perspectives of Knowledge Management
4.2.1 Technocentric
4.2.2 Organizational
4.2.3 Ecological4.3 What Drives Knowledge Management?4.3.1 Size and Dispersion of an Organization
4.3.2 Reducing Risk and Uncertainty
4.3.3 Improving the Quality of Decisions4.3.4 Improving Customer Relationships4.3.5 Technocentric Support
4.3.6 Intellectual Asset Management and Prevention of Knowledge Loss
4.3.7 Future Use of Knowledge4.3.8 Increase Market Value and Enhance an Organization’s Brand Image4.3.9 Shorter Product Cycles4.3.10 Restricted Access and Added Security
4.4 Typical Evolution of Knowledge Management within an Organization
4.4.1 Ad-hoc Knowledge4.4.2 Sophisticated Knowledge Management4.4.3 Embedded Knowledge Management4.4.4 Integrated Knowledge Management4.5 Elements of Knowledge Management
4.5.1 People and Skills
4.5.2 Procedures
4.5.3 Strategy and Policy4.5.4 Technology4.6 The Knowledge Management Process
4.6.1 Knowledge Discovery and Innovation
4.6.2 Knowledge Documentation
4.6.3 Knowledge Use4.6.4 Knowledge Sharing Through Pull and Push Technologies4.7 Knowledge Management Tools and Technologies4.7.1 Tools for Discovering Knowledge
4.7.2 Tools for Documenting Knowledge
4.7.3 Tools for Sharing and Using Knowledge
4.7.4 Technologies for Knowledge Management
4.8 Knowledge Management Measures
4.9 Knowledge Management Organization
4.10 Knowledge Management Roles and Responsibilities
4.10.1 Chief Knowledge Officer (CKO)
4.10.2 Knowledge Engineer (KE)4.10.3 Knowledge Facilitator (KF)4.10.4 Knowledge Worker (KW)
4.10.5 Knowledge Consultant (KC)
4.11 Knowledge Management Models
4.11.1 Transaction Model
4.11.2 Cognitive Model
4.11.3 Network Model
4.11.4 Community Model4.12 Models for Categorizing Knowledge4.12.1 Knowledge Spiral Model
4.12.2 Knowledge Management Model
4.12.3 Knowledge Category Model4.13 Models for Intellectual Capital Management
4.14 Socially Constructed Knowledge Management Models
4.15 Techniques to Model Knowledge
4.15.1 CommonKADS
4.15.2 Protégé 2000
4.16 K-commerce
4.17 Benefits of Knowledge Management
4.17.1 Knowledge-Related Benefits
4.17.2 Organizational and Administrative Benefits4.17.3 Individual Benefits4.18 Challenges of Knowledge Management
4.19 Warm-up Questions, Exercises, and Projects
Chapter 5 Fuzzy Logic
5.1 Introduction
5.2 Fuzzy Logic and Bivalued Logic
5.2.1 Fuzzy Versus Probability
5.3 Fuzzy Logic and Fuzzy Sets
5.4 Membership Functions
5.4.1 Fuzzification
5.4.2 Defuzzification
5.5 Operations on Fuzzy Sets
5.5.1 Intersection of Fuzzy Sets5.5.2 Union of Fuzzy Sets5.5.3 Complements of Fuzzy Sets
5.5.4 Equality of Fuzzy Sets
5.6 Types of Fuzzy Functions5.6.1 Quasi-Fuzzy Membership Functions5.6.2 Triangular Fuzzy Membership Functions
5.6.3 Trapezoidal Fuzzy Membership Function
5.7 Linguistic Variables
5.7.1 Linguistic Hedges
5.8 Fuzzy Relationships
5.9 Fuzzy Propositions
5.9.1 Fuzzy Connectives
5.10 Fuzzy Inference
5.11 Fuzzy Rules
5.12 Fuzzy Control System
5.13 Fuzzy Rule-Based System
5.13.1 Models of Fuzzy Rule-Based Systems5.14 Type-1 and Type-2 Fuzzy Rule-Based Systems
5.14.1 T2 FS Membership Functions
5.15 Modeling Fuzzy Systems
5.16 Limitations of Fuzzy Systems
5.17 Applications and Research Trends in Fuzzy Logic-Based Systems
5.18 Warm-up Questions, Exercises, and Projects
Chapter 6 Agent–Based Systems
6.1 Introduction
6.2 What is an Agent?
6.3 Characteristics of Agents
6.4 Advantages of Agent Technology
6.5 Agent Typologies
6.5.1 Collaborative Agent
6.5.2 Interface Agent
6.5.3 Mobile Agent
6.5.4 Information Agent
6.5.5 Hybrid Agent
6.6 Agent Communication Languages
6.7 Standard Communicative Actions
6.8 Agents and Objects
6.9 Agents, AI, and Intelligent Agents
6.10 Multiagent Systems
6.10.1 Layered Architecture of a Generic Multiagent System
6.11 Knowledge Engineering-Based Methodologies
6.11.1 MAS-CommonKADS
6.11.2 Desire
6.12 Case Study6.12.1 Partial Discharge Diagnosis within a GIS
6.12.2 Intelligent Agents for GIS Monitoring
6.13 Directions for Further Research
6.14 Warm-up Questions, Exercises, and Projects
Chapter 7 Connectionist Models
7.1 Introduction
7.1.1 Advantages and Disadvantages of Neural Networks
7.1.2 Comparing Artificial Neural Networks with the von Neumann Model
7.2 Biological Neurons
7.3 Artificial Neurons
7.4 Neural Network Architectures
7.4.1 Hopfield Model
7.4.2 Learning in a Hopfield Network Through Parallel Relaxation
7.4.3 Perceptrons
7.4.4 Perceptron Learning Rule
7.4.5 Fixed-Increment Perceptron Learning Algorithms
7.4.6 Multilayer Perceptrons
7.4.7 Back-Propagation Algorithms
7.5 Learning Paradigms
7.6 Other Neural Network Models
7.6.1 Kohonen Maps
7.6.2 Probabilistic Neural Networks
7.7 Integrating Neural Networks and Knowledge-Based Systems
7.8 Applications for Neural Networks
7.8.1 Applications for the Back-Propagation Model
7.9 Warm-up Questions, Exercises, and Projects
Chapter 8 Genetic Algorithms
8.1 Introduction
8.2 Basic Terminology
8.3 Genetic Algorithms
8.4 Genetic Cycles
8.5 Basic Operators of a Genetic Algorithm
8.5.1 Mutation
8.5.2 Crossover
8.5.3 Selection
8.6 Function Optimization
8.6.1 Stopping Criteria
8.7 Schema
8.7.1 Schema Defined
8.7.2 Instance, Defined Bits, and Order of Schema8.7.3 The Importance of Schema Results
8.8 Ordering Problems and Edge Recombination
8.8.1 Traveling Salesperson Problem
8.8.2 Solutions to Prevent Production of Invalid Offspring
8.8.3 Edge Recombination Technique
8.9 Island-Based Genetic Algorithms
8.10 Problem Solving Using Genetic Algorithms
8.11 Bayesian Networks and Genetic Algorithms
8.12 Applications and Research Trends in GA
8.13 Warm-up Questions, Exercises, and Projects
Chapter 9 Soft Computing Systems
9.1 Introduction to Soft Computing
9.2 Constituents of Soft Computing
9.3 Characteristics of Soft Computing
9.3.1 Simulation of Human Expertise9.3.2 Innovative Techniques9.3.3 Natural Evolution9.3.4 Model-Free Learning
9.3.5 Goal-Driven
9.3.6 Extensive Numerical Computations9.3.7 Dealing with Partial and Incomplete Information9.3.8 Fault Tolerance9.4 Neuro-Fuzzy Systems
9.4.1 Fuzzy Neural Networks
9.4.2 Cooperative Neuro-Fuzzy Model9.4.3 Concurrent Neuro-Fuzzy Model
9.4.4 Hybrid Neuro-Fuzzy Model
9.5 Genetic-Fuzzy Systems
9.5.1 Genetic Algorithms Controlled by Fuzzy Logic
9.5.2 Fuzzy Evolutionary Systems
9.5.3 Evolving Knowledge Bases and Rule Sets
9.6 Neuro-Genetic Systems
9.6.1 Neural Network Weight Training
9.6.2 Evolving Neural Nets
9.7 Genetic-Fuzzy-Neural Networks
9.8 Chaos Theory
9.8.1 Basic Constructs
9.8.2 Hybridization
9.9 Rough Set Theory
9.9.1 Pawlak’s Information System
9.9.2 Rough Sets
9.9.3 Rough Logic
9.9.4 Rough Models
9.9.5 Rough-Set–Based Systems
9.10 Applications of Soft Computing
9.11 Warm-up Questions, Exercises, and Projects
Chapter 10 Knowledge–Based Multiagent System Accessing Distributed Database Grid: An E–Learning Solution
10.1 Introduction and Background10.1.1 E-learning Defined
10.1.2 Major Components of E-learning
10.1.3 Objectives of E-learning
10.1.4 Advantages of E-learning10.2 Existing E-learning Solutions: Work Done so Far
10.3 Requirements for an Ideal E-learning Solution
10.3.1 Quality Parameters for an Ideal E-learning Solution
10.4 Toward a Knowledge-Based Multiagent Approach
10.4.1 Objectives of a Knowledge-Based Multiagent E-Learning Solution
10.4.2 Introduction to Multiagent Systems
10.4.3 Advantages of a Knowledge-Based Multiagent Approach for E-learning
10.5 System Architecture and Methodology
10.5.1 System Agents
10.5.2 Interaction Between Agents
10.5.3 Middleware Services
10.6 Knowledge Representation and System Output
10.7 Results of the Experiment
10.7.1 Advantages Achieved
10.8 Conclusion
Chapter 11 Knowledge–Intensive Learning: Diet Menu Planner
11.1 Introduction
11.2 Case Retrieval
11.2.1 The Identify Features11.2.2 Matching
11.3 Case Reuse
11.4 Case Revision
11.5 Case Retention
11.6 Organization of Cases in Memory
11.7 DietMaster
11.7.1 General Menu-Planning Process for Diabetic Patients
11.7.2 The DietMaster Architecture
11.8 Knowledge Model
11.9 Representation of Different Knowledge Types
11.9.1 Case Structure
11.9.2 General Knowledge
11.9.3 Rules
11.9.4 Procedures
11.10 Problem Solving in DietMaster
11.11 Integrated Reasoning in DietMaster
11.12 Problem Solving and Reasoning Algorithm
11.13 The Learning Process11.13.1 The Learning Algorithm
11.14 Feedback on Diet Plan
11.15 Conclusion
Chapter 12 Natural Language Interface: Question Answering System
12.1 Introduction
12.1.1 Open-Domain Question Answering
12.1.2 Closed-Domain Question Answering
12.2 Natural Language Interface to Structured Data
12.3 Natural Language Interface to Unstructured Data
12.4 Different Approaches to Language
12.4.1 Symbolic (Rule-Based) Approach
12.4.2 Empirical (Corpus-Based) Approach
12.4.3 Connectionist Approach (Usingsing a Neural Network)
12.5 Semantic-Level Problems
12.6 Shallow Parsing
12.6.1 Semantic Symmetry
12.6.2 Sentence Patterns and Semantic Symmetry
12.6.3 An Algorithm
12.7 Ambiguous Modification
12.8 Conclusion
Index

Content preview from Knowledge-Based Systems

336 Chapter 12 Natural laNguage INterfaCe: QuestIoN aNswerINg system

Most of the empirical NLP methods employ statistical techniques, such

as n-gram models, hidden Markov models (HMMs), and probabilistic con-

text-free grammars (PCFGs). Given the successes of empirical NLP meth-

ods, researchers have begun to apply learning methods to the construction of

information extraction systems (McCarthy, 1995; Soderland, 1995). Several

different symbolic and statistical methods have been employed, but most of

them are used to generate one part of a larger information extraction system.

Majumder (2002) experimented with n-gram-based language modeling and

claimed to develop a language-independent approach to IR and natural lan-

guage processing.

12.4.3 ...

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Knowledge-Based Systems

by Rajendra Akerkar, Priti Sajja

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