Abstract Dialectical Frameworks are Boolean Networks<sup>*</sup>

Dialectical frameworks are a unifying model of formal argumentation, where argumentative relations between arguments are represented by assigning acceptance conditions to atomic arguments. Their generality allows them to cover a number of different approaches with varying forms of representing the argumentation structure. Boolean regulatory networks are used to model the dynamics of complex biological processes, taking into account the interactions of biological compounds, such as proteins or genes. These models have proven highly useful for comprehending such biological processes, allowing to reproduce known behaviour and testing new hypotheses and predictions in silico, for example in the context of new medical treatments. While both these approaches stem from entirely different communities, it turns out that there are striking similarities in their appearence. In this paper, we study the relation between these two formalisms revealing their communalities as well as their differences, and introducing a correspondence that allows to establish novel results for the individual formalisms.

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Open University of the Netherlands Research Portal
Published
2024-11-02
Primary Topic
Gene Regulatory Network Analysis
Type
article
Field-Weighted Citation Impact
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article

Abstract Dialectical Frameworks are Boolean Networks*

Open University of the Netherlands Research Portal
Gene Regulatory Network Analysis
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Abstract Dialectical Frameworks are Boolean Networks*

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Abstract

Dialectical frameworks are a unifying model of formal argumentation, where argumentative relations between arguments are represented by assigning acceptance conditions to atomic arguments. Their generality allows them to cover a number of different approaches with varying forms of representing the argumentation structure. Boolean regulatory networks are used to model the dynamics of complex biological processes, taking into account the interactions of biological compounds, such as proteins or genes. These models have proven highly useful for comprehending such biological processes, allowing to reproduce known behaviour and testing new hypotheses and predictions in silico, for example in the context of new medical treatments. While both these approaches stem from entirely different communities, it turns out that there are striking similarities in their appearence. In this paper, we study the relation between these two formalisms revealing their communalities as well as their differences, and introducing a correspondence that allows to establish novel results for the individual formalisms.

Open University of the Netherlands Research Portal
Intelligent Systems Research (United States) (US), Universidade Nova de Lisboa (PT)
NOVA Laboratory for Computer Science and Informatics, Fundação para a Ciência e a Tecnologia
Openalex Percentile: Top 53%
Gene Regulatory Network Analysis
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