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Table of contents
1 Introduction
1.1 Context
1.1.1 A brief history of decision-making systems
1.1.2 Business rule management systems
1.1.3 Explanation in rule-based systems
1.2 Motivations and objectives
1.3 Thesis outlines
2 Rule-Based systems, BRMS and decision automation
2.1 General information about rule-based systems
2.2 Rules in decision automation
2.2.1 Business rules: basic denitions
2.2.2 Basic architecture for rule-based systems
2.3 The IBM business rules management system
2.3.1 Hierarchy of decision service in IBM ODM
2.3.2 IBM Operational Decision Manager: platform and architecture
2.3.3 The rule engine of IBM ODM
2.3.4 Applications
2.4 Discussion and conclusions
3 An overview of explanation in rule-based Systems
3.1 The need for explanation
3.2 Philosophical background on the theory of explanation
3.3 Explanations in rule-based systems
3.3.1 Categorization by temporal context / explanation orientation
3.3.2 Type of questions
3.3.3 Content types of explanations
3.3.4 Context sensitivity
3.4 A quick historical overview of expert systems with explanation capabilities
3.5 Causality in rule-based systems
3.5.1 Causal explanations in rule-based systems
3.5.2 Choosing a causal model / formal model for causal ascription
3.6 Requirements for an IBM ODM explanation feature
4 A simplied causal model for rule-based systems
4.1 Introduction
4.2 Concepts and denitions
4.2.1 Business rules
4.2.2 Towards a normalized business rule formalism
4.2.3 Orchestration and execution of the business rules
4.2.4 Tracing the process: what should be in the decision trace?
4.3 Representing causality in business rule-based systems
4.3.1 Events typology in a business rule decision
4.3.2 Dening the signature of a rule-based system
4.3.3 Causality between events
4.3.4 Typology of the relations
4.3.5 Hierarchical causal model
4.4 Process of construction of a minimal causal model
4.4.1 Causal model of the system and list of relevant events
4.4.2 Minimal decision trace
4.4.3 Minimal causal model of the decision
4.4.4 Conclusion
5 Engineering the causal model
5.1 A Framework for Causal Ascription and Representation in Rule-Based System
5.1.1 Encoding business rules (BR) into BR-Objects
5.1.2 Extracting the business rules of a decision project
5.1.3 Ascribing causal relations between the business rule elements .
5.1.4 Recording the minimal trace of a decision
5.1.5 Constructing the minimal causal model of a decision
5.2 Experiments and assessment protocol
5.2.1 Reduction for the business rule-based system’s causal model .
5.2.2 Reduction for minimal traces of the decision
5.3 Conclusion
6 Towards an architecture of an explanation service for business rulebased systems: basics and insights
6.1 Introduction
6.2 The basics towards a service architecture to support explanatory models150
6.3 The components of an explanatory model
6.3.1 Conceptual model as a part of the explanatory model
6.3.2 User model as a part of the explanatory model
6.4 Exploitation of the explanatory model: discussion and insights
6.4.1 How we deal with each criterion
6.4.2 A graphical representation for engineering the explanation .
7 Conclusion
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