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Z. G. Saribatur, J. P. Wallner, S. Woltran:
"Explaining Non-Acceptability in Abstract Argumentation";
Talk: European Conference on Artificial Intelligence (ECAI), Santiago de Compostela, Spain; 2020-06-08 - 2020-06-12; in: "Proceedings ECAI", G. De Giacomo (ed.); IOS Press, 325 (2020), ISBN: 978-1-64368-101-6; 881 - 888.

Argumentation frameworks (AFs) provide a central approach to perform reasoning in many formalisms within argumentation in Artificial Intelligence (AI). Semantics for AFs specify criteria under which sets of arguments can be deemed acceptable, with the notion of admissibility being at the core of main semantics for AFs. A fundamental reasoning task is to find an admissible set containing a queried argument, called credulous acceptance under admissibility. While such a set explains how to argue in favour of the queried argument, finding an explanation in the negative case, i.e.\ why the queried argument is not credulously accepted under admissibility, is less immediate. In this paper, we approach this problem by considering subframeworks of a given AF as witnesses for non-acceptability. Due to the non-monotonicity of semantics for AFs, this requires that every expansion of the witnessing subframework must preserve non-acceptance of the argument---otherwise the subframework would not give sufficient reason for rejection. Among our main contributions, (i) we show that this notion of witnessing subframeworks is connected to strong admissibility of AFs, (ii) we investigate the complexity of finding small such subframeworks, and (iii) we extend a recently proposed framework for abstraction in the declarative answer-set programming paradigm in order to compute such strongly rejecting subframeworks. The resulting system is thus able to deliver explanations also in the case of non-acceptance and we provide a first empirical study that shows the feasibility of our approach.

http://dx.doi.org/10.3233/FAIA200179

Project Head Stefan Szeider:
DK - Logic

Project Head Johannes Peter Wallner:
EMBArg

Project Head Stefan Woltran:
START