Predictive Models and Abstract Argumentation: the case of High-Complexity Semantics

In this paper we describe how predictive models can be positively exploited in abstract
argumentation. In particular, we present two main sets of results. On one side, we show
that predictive models are effective for performing algorithm selection in order to determine which approach is better to enumerate the preferred extensions of a given argumentation framework. On the other side, we show that predictive models predict significant aspects of the solution to the preferred extensions enumeration problem. By exploiting an extensive set of argumentation framework features— i.e., values that summarise a potentially important property of a framework—the proposed approach is able to provide an accurate prediction about which algorithm would be faster on a given problem instance, as well as of the structure of the solution, where the complete knowledge of such structure would require a computationally hard problem to be solved. Improving the ability of existing argumentation-based systems to support human
sense-making and decision processes is just one of the possible exploitations of such knowledge
obtained in an inexpensive way.