Probabilistic Control Argumentation Frameworks

In this paper we present Probabilistic Control Argumentation Frameworks (PCAFs) that extend classical Control Argumentation Frameworks (CAFs) to take into account probabilistic information in the reasoning process. We show that probabilities can be used to optimally control CAFs that cannot be controlled otherwise. We introduce the notion of controlling power, that represents the probability that a control configuration reaches its target. A computational method based on Monte Carlo simulations for computing the controlling power of control configurations is defined. We experimentally show that PCAFs outperform w.r.t runtime classical CAFs and in a large number of situations they can reach the target with a high probability while the classical CAFs fail.