Learning quantitative argumentation frameworks using sparse neural networks and swarm intelligence algorithms

Abstract

Argumentation Frameworks are an approach of formalizing arguments and their interrelations in a graph structure. They can be used to draw conclusions from this modelling of knowledge. Since argumentation is an important part of human reasoning, these graph structures can be considered easily interpretable, what makes this technology an interesting explainable artificial intelligence method. Although this is not their main purpose, Quantitative Argumentation Frameworks can be used to solve classification problems by following a new approach. This approach is based on constructing them out of multilayer perceptrons (MLP), based on the work of Potyka. In this thesis we were motivated to construct Quantitative Argumentation Frameworks out of sparse MLPs. A swarm intelligence algorithm, namely Particle Swarm Optimization (PSO), was developed to search for sparse MLP models with specific characteristics that relate to performance and topology of the graphical structures. Models were implemented, tested, and evaluated on three different datasets. The implementation includes preprocessing of the datasets, parameter learning of MLPs based on backpropagation, and structure learning of the MLP graphical structures. The evaluation involves constructing fully connected MLPs and decision trees for comparison purposes. The resulting models achieved high performance and low complexity in their structure. The PSO algorithm also proved its efficiency in solving the structure learning