Probabilistic Argumentation with Incomplete Information
Anthony Hunter, Matthias Thimm · Frontiers in artificial intelligence and applications · 2014
We consider augmenting abstract argumentation frame-works with probabilistic information and discuss different constraints to obtain meaningful probabilistic information. Moreover, we investigate the problem of incomplete probability assignments and propose a solution for completing these assignments by applying the principle of maximum entropy.