Knowledge Integration Methods for Probabilistic Knowledge-based Systems

Cover
Half Title
Title Page
Copyright Page
Contents
Preface
Authors
CHAPTER 1: Introduction
	1.1. MOTIVATION
	1.2. THE OBJECTIVES OF THIS BOOK
	1.3. THE STRUCTURE OF THIS BOOK
CHAPTER 2: Probabilistic knowledge-based systems
	2.1. KNOWLEDGE BASE REPRESENTATION
		2.1.1. Knowledge Representation Methods
		2.1.2. Probabilistic Knowledge Base Representation
	2.2. TYPES OF KNOWLEDGE-BASED SYSTEMS
	2.3. THE KNOWLEDGE-BASED SYSTEM DEVELOPMENT
	2.4. COMPONENTS OF A PROBABILISTIC KNOWLEDGE-BASED SYSTEM
	2.5. COMPARING PROBABILISTIC KNOWLEDGE-BASED SYSTEM WITH OTHER SYSTEMS
	2.6. CONCLUDING REMARKS
CHAPTER 3: Inconsistency measures for probabilistic knowledge bases
	3.1. OVERVIEW OF INCONSISTENCY MEASURES
		3.1.1. Distance Functions
		3.1.2. Development of Inconsistency Measures
	3.2. REPRESENTING THE INCONSISTENCY OF THE PROBABILISTIC KNOWLEDGE BASE
		3.2.1. Basic Notions
		3.2.2. Characteristic Model
		3.2.3. Desired Properties of Inconsistency Measures
	3.3. INCONSISTENCY MEASURES FOR PROBABILISTIC KNOWLEDGE BASES
		3.3.1. The Basic Inconsistency Measures
		3.3.2. The Norm-based Inconsistency Measures
		3.3.3. The Unnormalized Inconsistency Measure
	3.4. ALGORITHMS FOR COMPUTING THE INCONSISTENCY MEASURES
		3.4.1. The Computational Complexity
		3.4.2. The General Methods
		3.4.3. Algorithms
	3.5. CONCLUDING REMARKS
CHAPTER 4: Methods for restoring consistency in probabilistic knowledge bases
	4.1. OVERVIEW OF HANDLING INCONSISTENCIES
		4.1.1. The Inconsistency Resolution Problem
		4.1.2. Methods of Handling Inconsistencies
	4.2. RESTORING CONSISTENCY IN PROBABILISTIC KNOWLEDGE BASES
		4.2.1. Basic Notions
		4.2.2. Desired Properties of Consistency-Restoring Operator
		4.2.3. A General Model for Restoring Consistency
	4.3. METHODS FOR RESTORING CONSISTENCY
		4.3.1. The Norm-based Consistency-restoring Problem
		4.3.2. The Unnormalized Consistency-Restoring Problem
	4.4. ALGORITHMS FOR RESTORING CONSISTENCY
	4.5. CONCLUDING REMARKS
CHAPTER 5: Distance-based methods for integrating probabilistic knowledge bases
	5.1. OVERVIEW OF KNOWLEDGE INTEGRATION METHODS
		5.1.1. The Knowledge Integration Problem
		5.1.2. Methods for Integrating Knowledge Bases
	5.2. PROBABILISTIC KNOWLEDGE INTEGRATION
		5.2.1. Divergence Functions
		5.2.2. Distance-based Model for Integrating Probabilistic Knowledge Bases
		5.2.3. Desired Properties of Distance-based Probabilistic Integrating Operator
		5.2.4. Finding the Satisfying Probability Vector
	5.3. THE PROBLEMS WITH DISTANCE-BASED INTEGRATING PROBABILISTIC KNOWLEDGE BASES
	5.4. DISTANCE-BASED INTEGRATING OPERATORS
		5.4.1. The Class of Probabilistic Integrating Operators Γϑ
		5.4.2. The Class of Probabilistic Integrating Operators ΓHU
	5.5. INTEGRATION ALGORITHMS
		5.5.1. Algorithm for Finding the Satisfying Probability Vector
		5.5.2. The Distance-based Integration Algorithm
		5.5.3. The HULL Algorithm
	5.6. CONCLUDING REMARKS
CHAPTER 6: Value-based method for integrating probabilistic knowledge bases
	6.1. VALUE-BASED PROBABILISTIC KNOWLEDGE INTEGRATION
		6.1.1. Basic Notions
		6.1.2. Value-based Model for Integrating Probabilistic Knowledge Bases
		6.1.3. Desired Properties of Value-based Probabilistic Integrating Operator
	6.2. THE PROBABILITY VALUE-BASED INTEGRATING OPERATORS
	6.3. THE PROBABILITY VALUE-BASED INTEGRATION ALGORITHMS
		6.3.1. Algorithm for Deducting Probabilistic Constraints
		6.3.2. Probability Value-based Integration Algorithms
	6.4. CONCLUDING REMARKS
CHAPTER 7: Experiments and Applications
	7.1. EXPERIMENT
		7.1.1. Experimental Purpose and Assumptions
		7.1.2. Experiment Settings
		7.1.3. Experimental Implementation
		7.1.4. Results and Analysis
	7.2. APPLICATIONS
		7.2.1. Artificial Intelligence and Machine Learning
			7.2.1.1. Machine Learning
			7.2.1.2. Recommendation Systems
			7.2.1.3. Group Decision-making
		7.2.2. Knowledge Systems
		7.2.3. Software Engineering
		7.2.4. Other Applications
CHAPTER 8: Conclusions and open problems
	8.1. CONCLUSIONS
	8.2. OPEN PROBLEMS
Bibliography
Index