An Implementation, Empirical Evaluation and Proposed Improvement for Bidirectional Splitting Method for Argumentation Frameworks under Stable Semantics

TL;DR

This paper evaluates bidirectional splitting methods for argumentation frameworks under stable semantics, finding that balanced cuts outperform minimum cuts in computational efficiency.

Contribution

It implements and empirically tests bidirectional splitting with different graph cut algorithms, proposing balanced cuts as a novel improvement for better performance.

Findings

Balanced cuts significantly improve computation speed.

Minimum cuts do not enhance performance in most cases.

Empirical evaluation supports the proposed method.

Abstract

Abstract argumentation frameworks are formal systems that facilitate obtaining conclusions from non-monotonic knowledge systems. Within such a system, an argumentation semantics is defined as a set of arguments with some desired qualities, for example, that the elements are not in conflict with each other. Splitting an argumentation framework can efficiently speed up the computation of argumentation semantics. With respect to stable semantics, two methods have been proposed to split an argumentation framework either in a unidirectional or bidirectional fashion. The advantage of bidirectional splitting is that it is not structure-dependent and, unlike unidirectional splitting, it can be used for frameworks consisting of a single strongly connected component. Bidirectional splitting makes use of a minimum cut. In this paper, we implement and test the performance of the bidirectional splitting method, along with two types of graph cut algorithms. Experimental data suggest that using a minimum cut will not improve the performance of computing stable semantics in most cases. Hence, instead of a minimum cut, we propose to use a balanced cut, where the framework is split into two sub-frameworks of equal size. Experimental results conducted on bidirectional splitting using the balanced cut show a significant improvement in the performance of computing semantics.