Learning Gradual Argumentation Frameworks using Genetic Algorithms

by   Jonathan Spieler, et al.

Gradual argumentation frameworks represent arguments and their relationships in a weighted graph. Their graphical structure and intuitive semantics makes them a potentially interesting tool for interpretable machine learning. It has been noted recently that their mechanics are closely related to neural networks, which allows learning their weights from data by standard deep learning frameworks. As a first proof of concept, we propose a genetic algorithm to simultaneously learn the structure of argumentative classification models. To obtain a well interpretable model, the fitness function balances sparseness and accuracy of the classifier. We discuss our algorithm and present first experimental results on standard benchmarks from the UCI machine learning repository. Our prototype learns argumentative classification models that are comparable to decision trees in terms of learning performance and interpretability.


page 1

page 2

page 3

page 4


Value Based Argumentation Frameworks

This paper introduces the notion of value-based argumentation frameworks...

Interpreting Neural Networks as Gradual Argumentation Frameworks (Including Proof Appendix)

We show that an interesting class of feed-forward neural networks can be...

Deep Learning for Abstract Argumentation Semantics

In this paper, we present a learning-based approach to determining accep...

Handling controversial arguments by matrix

We introduce matrix and its block to the Dung's theory of argumentation ...

Forecasting Argumentation Frameworks

We introduce Forecasting Argumentation Frameworks (FAFs), a novel argume...

Retaining Experience and Growing Solutions

Generally, when genetic programming (GP) is used for function synthesis ...

A Concept and Argumentation based Interpretable Model in High Risk Domains

Interpretability has become an essential topic for artificial intelligen...