Impact Measures for Gradual Argumentation Semantics
This work addresses the need for better interpretation tools in argumentation frameworks, but it is incremental as it builds on existing measures and semantics.
The paper tackled the problem of interpreting outcomes in gradual argumentation semantics by refining an existing impact measure and introducing a new one based on Shapley values, analyzing them against known semantics to provide deeper insights.
Argumentation is a formalism allowing to reason with contradictory information by modeling arguments and their interactions. There are now an increasing number of gradual semantics and impact measures that have emerged to facilitate the interpretation of their outcomes. An impact measure assesses, for each argument, the impact of other arguments on its score. In this paper, we refine an existing impact measure from Delobelle and Villata and introduce a new impact measure rooted in Shapley values. We introduce several principles to evaluate those two impact measures w.r.t. some well-known gradual semantics. This comprehensive analysis provides deeper insights into their functionality and desirability.