A Unifying Framework for Learning Argumentation Semantics
Zlatina Mileva Antonis Bikakis Fabio Aurelio D'Asaro Mark Law Alessandra Russo
Argumentation is a very active research field of Artificial Intelligence
concerned with the representation and evaluation of arguments used in dialogues
between humans and/or artificial agents. Acceptability semantics of formal
argumentation systems define the criteria for the acceptance or rejection of
arguments. Several software systems, known as argumentation solvers, have been
developed to compute the accepted/rejected arguments using such criteria. These
include systems that learn to identify the accepted arguments using
non-interpretable methods. In this paper we present a novel framework, which
uses an Inductive Logic Programming approach to learn the acceptability
semantics for several abstract and structured argumentation frameworks in an
interpretable way. Through an empirical evaluation we show that our framework
outperforms existing argumentation solvers, thus opening up new future research
directions in the area of formal argumentation and human-machine dialogues.