Coalitional games for abstract argumentation
Elise Bonzon, Nicolas Maudet, Stefano Moretti · Frontiers in artificial intelligence and applications · 2014
In this work we address the issue of uncertainty in abstract argumentation. We propose a way to compute the relative relevance of arguments by merging the classical argumentation framework proposed in [5] into a game theoretic coalitional setting, where the worth of a collection of arguments can be seen as the combination of the information concerning the defeat relation and the preferences over arguments of a “user”. Via a property-driven approach, we show that the Shapley value [17] for coalitional games defined over an argumentation framework, can be applied to resume all the information about the worth of sets of arguments into an attribution of relevance for the single arguments. We also prove that, for a large family of (coalitional) argumentation frameworks, the Shapley value can be easily computed.