Knowledge Representation and Inductive Reasoning using Conditional Logic and Sets of Ranking Functions

Title Page
Contents
Chapter 1. Introduction
	1.1 Context and Motivation
	1.2 Research Questions and Contributions
	1.3 Outline
	1.4 Previous Publications
Chapter 2. Background
	2.1 Logical Preliminaries
	2.2 From Preferential Inference to Plausibility Measures
	2.3 Ranking Functions
	2.4 Inductive Reasoning in System Z
Chapter 3. Inference Using Sets of Ranking Functions
	3.1 Modes of Inference
	3.2 C-Representations and C-Inference
	3.3 Interrelationships of Inference Systems
Chapter 4. Classification of Conditionals for Calculating Closures of Inference Relations
	4.1 Classes of Conditionals
	4.2 Complete Inference Relations
Chapter 5. Inference Cores and Redundant Conditionals
	5.1 Inference Cores for Comparing Inference Relations
	5.2 Structural Inference and Redundant Conditionals
Chapter 6. Maximal Impacts for C-Inference
	6.1 Regular and Sufficient Maximal Impacts
	6.2 Lower and Upper Bounds for Regular and Sufficient Maximal Impacts
Chapter 7. Compact Representations of Knowledge Bases for Optimising C-Inference
	7.1 Representing C-Inference as CSPs
	7.2 Compact Representation of Static Knowledge Bases
	7.3 Computational Benefits
	7.4 Compact Representation of Evolving Knowledge Bases
Chapter 8. Formal Properties and Evaluation of Nonmonotonic Inference Relations
	8.1 Skeptical Inference
	8.2 Credulous Inference
	8.3 Weakly Skeptical Inference
	8.4 Rationality of C-Inference Relations
	8.5 Empirical Evaluation of Nonmonotonic Inference Relations
Chapter 9. InfOCF: Implementing Inference Over Sets of Ranking Models
	9.1 InfOCF-Lib
	9.2 Implementing EvaluateKBs(RM)
	9.3 Applications, Expansions and Future Work
Chapter 10. Conclusions, Open Questions and Final Remarks
	10.1 Summary
	10.2 Future Work and Outlook
Bibliography