Inverse Problems for Gradual Semantics

Nir Oren (1) , Bruno Yun (1) , Srdjan Vesic (2) , Murilo Baptista (1)

1 CSD - Department of Computing Science - University of Aberdeen
2 CRIL - Centre de Recherche en Informatique de Lens

Gradual semantics with abstract argumentation provide each argument with a score reflecting its acceptability. Many different gradual semantics have been proposed in the literature, each following different principles and producing different argument rankings. A sub-class of such semantics, the so-called weighted semantics, takes, in addition to the graph structure, an initial set of weights over the arguments as input, with these weights affecting the resultant argument ranking. In this work, we consider the inverse problem over such weighted semantics. That is, given an argumentation framework and a desired argument ranking, we ask whether there exist initial weights such that a particular semantics produces the given ranking. The contribution of this paper are: (1) an algorithm to answer this problem, (2) a characterisation of the properties that a gradual semantics must satisfy for the algorithm to operate, and (3) an empirical evaluation of the proposed algorithm.

Domaines: Intelligence artificielle [cs.AI]

Nir Oren, Bruno Yun, Srdjan Vesic, Murilo Baptista. Inverse Problems for Gradual Semantics. Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}, Jul 2022, Vienna, Austria. pp.2719-2725, ⟨10.24963/ijcai.2022/377⟩. ⟨hal-03768129⟩