Integrating Defeasible Argumentation and Machine Learning Techniques: Difference between revisions

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Created page with "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 t..."
 
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Title: 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?
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.


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


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.
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.


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.
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.


Link to Article: https://arxiv.org/abs/0402057v1
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:  
Authors:  
arXiv ID: 0402057v1
arXiv ID: 0402057v2


[[Category:Computer Science]]
[[Category:Computer Science]]
[[Category:Machine]]
[[Category:Learning]]
[[Category:Techniques]]
[[Category:Techniques]]
[[Category:Defeasible]]
[[Category:Defeasible]]
[[Category:Argumentation]]
[[Category:Argumentation]]
[[Category:Machine]]
[[Category:Learning]]

Latest revision as of 15:28, 24 December 2023

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

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