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Transforming Natural Arguments in Araucaria to Formal Arguments in LMA

Authors: Yohsuke Takahashi; Hajime Sawamura; Jing Zhang;

Transforming Natural Arguments in Araucaria to Formal Arguments in LMA

Abstract

Over the last few years, argumentation has been gaining increasing importance in multi-agent systems research. LMA is a Logic of multiple-valued argumentation built on top of EALP (extended annotated logic programming). Araucaria is a software tool for analyzing and diagramming arguments written in natural language. We integrate these two systems involved in argumentation to an easy to use and more user-friendly argumentation system, in which natural arguments can be transformed into formal arguments, in order to lighten users' knowledge burden forced by formal argumentation frameworks, and attain extensive usability of formal argumentation systems. We illustrate the transformation process by translating natural arguments in English to formal arguments in EALP step-by-step, and then how the status of those formal arguments can be determined in LMA, resulting in the status determination of original natural arguments.

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Powered by OpenAIRE graph
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
3
Average
Top 10%
Average
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