A Basic Framework for Explanations in Argumentation

We discuss explanations for formal (abstract and structured) argumentation—the question of whether and why a certain argument or claim can be accepted (or not) under various extension-based semantics. We introduce a flexible framework, which can act as the basis for many different types of explanations. For example, we can have simple or comprehensive explanations in terms of arguments for or against a claim, arguments that (indirectly) defend a claim, the evidence (knowledge base) that supports or is incompatible with a claim, and so on. We show how different types of explanations can be captured in our basic framework, discuss a real-life application and formally compare our framework to existing work.

Authors

• Annemarie Borg
• Floris Bex

Keywords

• Semantics
• Knowledge based systems
• Intelligent systems
• Machine learning algorithms
• Law enforcement
• Knowledge representation
• Augmented reality
• Semantic
• Knowledge Base
• Learning Algorithms
• Real-life Applications
• Explainable Artificial Intelligence
• Argument Structure
• Set Of Arguments
• Type Of Explanation
• Formal Argument
• Part Of The Explanation
• Acceptable Strategy
• Form Of Explanation
• Basic Explanation
• Artificial intelligence
• knowledge representation formalisms and methods
• nonmonotonic reasoning and belief revision