Integrating Defeasible Argumentation and Machine Learning Techniques

Title: Integrating Defeasible Argumentation and Machine Learning Techniques

Research Question: How can defeasible argumentation and machine learning techniques be integrated to enhance the extraction and interpretation of knowledge from data?

Methodology: The study combines defeasible argumentation, a method for formalizing qualitative reasoning, with machine learning techniques, which are used for quantitative reasoning. The authors suggest various ways to integrate these two approaches, focusing on a generic argument-based framework. They propose a specific setting for text mining problems, where they apply their approach to extract and interpret knowledge from textual data.

Results: The authors present a preliminary report outlining the potential benefits of integrating defeasible argumentation and machine learning techniques. They discuss the common aspects of argument-based frameworks and machine learning techniques, and propose a dialectical reasoning process that resolves conflicts between arguments by applying a preference criterion.

Implications: The integration of defeasible argumentation and machine learning techniques has the potential to enhance the extraction and interpretation of knowledge from data. By combining the qualitative reasoning capabilities of defeasible argumentation with the quantitative reasoning abilities of machine learning techniques, the authors hope to create more robust and adaptable systems for knowledge extraction and interpretation. This approach could have significant implications for various fields, including natural language processing, legal reasoning, and multi-agent systems.

Link to Article: https://arxiv.org/abs/0402057v1 Authors: arXiv ID: 0402057v1