Inverse Problems for Gradual Semantics

Nir Oren, Bruno Yun, Srdjan Vesic, Murilo Baptista

Research output: Chapter in Book/Report/Conference proceedingPublished conference contribution

3 Citations (Scopus)

Abstract

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.
Original languageEnglish
Title of host publicationProceedings of the Thirty-First International Joint Conference on Artificial Intelligence
EditorsLuc De Raedt
PublisherIJCAI
Pages2719-2725
Number of pages7
ISBN (Electronic)978-1-956792-00-3
DOIs
Publication statusPublished - 16 Jul 2022
Event31st International Joint Conference on Artificial Intelligence: and the 25th European Conference on Artificial Intelligence - Messe Wien, Vienna, Austria
Duration: 23 Jul 202229 Jul 2022
Conference number: 31
https://ijcai-22.org/

Conference

Conference31st International Joint Conference on Artificial Intelligence
Abbreviated titleIJCAI-ECAI 2022
Country/TerritoryAustria
CityVienna
Period23/07/2229/07/22
Internet address

Keywords

  • Knowledge Representation and Reasoning: Argumentation
  • Agent-based and Multi-agent Systems: Agreement Technologies: Argumentation

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