Integrating Defeasible Argumentation and Machine Learning Techniques

Title: Integrating Defeasible Argumentation and Machine Learning Techniques

Abstract: This research explores the integration of defeasible argumentation and machine learning techniques. The goal is to combine the qualitative reasoning capabilities of defeasible argumentation with the data-driven approach of machine learning. The study outlines various directions for integrating these techniques and discusses their application in text mining problems.

Main Research Question: How can defeasible argumentation and machine learning techniques be integrated to enhance the reasoning capabilities of artificial intelligence systems?

Methodology: The study begins by introducing the components of an argument-based framework, which serves as the foundation for the integration. It then outlines the possible directions for integrating ML techniques and argument-based frameworks. The paper focuses on a specific setting, text mining problems, and discusses the application of such an approach.

Results: The research demonstrates that the integration of defeasible argumentation and machine learning techniques can lead to enhanced reasoning capabilities in artificial intelligence systems. The study highlights the potential benefits of this integration, particularly in the context of text mining problems.

Implications: The integration of defeasible argumentation and machine learning techniques has significant implications for the field of artificial intelligence. It can lead to more robust and flexible systems that can handle a wider range of problems. The study also highlights the potential applications of this approach in various domains, such as natural language processing and legal reasoning.

Conclusion: In conclusion, the integration of defeasible argumentation and machine learning techniques offers a promising approach to enhancing the reasoning capabilities of artificial intelligence systems. The study provides a roadmap for future research in this area and highlights the potential applications of this approach in various domains.

Link to Article: https://arxiv.org/abs/0402057v2 Authors: arXiv ID: 0402057v2