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argumentation frameworks are formal systems that facilitateobtaining conclusions from non-monotonic knowledge systems. Within such asystem, an argumentation semantics is defined as a set of arguments with somedesired qualities, for example, that the elements are not in conflict with eachother. Splitting an argumentation framework can efficiently speed up thecomputation of argumentation semantics. With respect to stable semantics, twomethods have been proposed to split an argumentation framework either in aunidirectional or bidirectional fashion. The advantage of bidirectionalsplitting is that it is not structure-dependent and, unlike unidirectionalsplitting, it can be used for frameworks consisting of a single stronglyconnected component. Bidirectional splitting makes use of a minimum cut. Inthis paper, we implement and test the performance of the bidirectionalsplitting method, along with two types of graph cut algorithms. Experimentaldata suggest that using a minimum cut will not improve the performance ofcomputing stable semantics in most cases. Hence, instead of a minimum cut, wepropose to use a balanced cut, where the framework is split into twosub-frameworks of equal size. Experimental results conducted on bidirectionalsplitting using the balanced cut show a significant improvement in theperformance of computing semantics.

international journal of artificial intelligence and applications

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