On Diagnostic Arguments in Abstract Argumentation

We are interested in employing argumentation for intelligence analysis due to its obvious potential for real-world impact. The Analysis of Competing Hypotheses, a well-known technique for multiple hypothesis evaluation from the intelligence community, includes sensitivity analysis as a task which helps analysts identify diagnostic information. We draw upon this notion of sensitivity analysis in this paper to set out a novel algorithm, called the Diagnostic Argument Identifier, that is able to identify diagnostic arguments. We employ a labelling-based approach to compute acceptance probabilities between partitions of arguments, which are then used to calculate the mutual information between the labels of each partition before and after the sequential removal of each argument from a framework. We present the results from one experiment on an abstract framework to assess whether our method can identify diagnostic arguments, and thus aid intelligence analysts. We argue that our algorithmic approach systematises and, therefore, reduces the subjectivity of sensitivity analysis; thus, yielding benefits to intelligence analysts – or any other expert working within a decision or deliberation setting – who need to objectively reevaluate the dependence of their set of conclusions on observed data present within an analysis.