A Filtering-based General Approach to Learning Rational Constraints of Epistemic Graphs

TL;DR

This paper introduces a filtering-based method for learning rational constraints in epistemic graphs, enabling the extraction of diverse, rational rules from data while maintaining computational efficiency.

Contribution

It presents a novel filtering-based approach with multiple generalization steps to learn rational rules consistent with epistemic graphs, improving over previous methods.

Findings

Outperforms existing frameworks in rule variety expansion

Effectively learns rational rules reflecting domain and user information

Enhances computational efficiency by excluding meaningless rules

Abstract

Epistemic graphs are a generalization of the epistemic approach to probabilistic argumentation. Hunter proposed a 2-way generalization framework to learn epistemic constraints from crowd-sourcing data. However, the learnt epistemic constraints only reflect users' beliefs from data, without considering the rationality encoded in epistemic graphs. Meanwhile, the current framework can only generate epistemic constraints that reflect whether an agent believes an argument, but not the degree to which it believes in it. The major challenge to achieving this effect is that the computational complexity will increase sharply when expanding the variety of constraints, which may lead to unacceptable time performance. To address these problems, we propose a filtering-based approach using a multiple-way generalization step to generate a set of rational rules which are consistent with their epistemic graphs from a dataset. This approach is able to learn a wider variety of rational rules that reflect information in both the domain model and the user model. Moreover, to improve computational efficiency, we introduce a new function to exclude meaningless rules. The empirical results show that our approach significantly outperforms the existing framework when expanding the variety of rules.