Ranking semantics based on subgraphs analy

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We propose a set of novel ranking-based semantics based on a measure of the sensitivity of each argument in an abstract argumentation framework. The sensitivity index is an indicator of how sensitive the label assigned to an argument by an argumentation semantics is, and it is derived from the topology of the graph via a subgraphs analysis coupled with the postulates of the chosen semantics. Using the total rank on arguments induced by such indicator, we propose two ranking-based semantics. We compare the behaviour of our semantics with recent proposals and a widespread set of properties identified in literature. A key feature of our semantics is that the attack relation between arguments keeps the same meaning as found in Dung's abstract semantics. By still relying on Dung's semantics we can soundly deal with any graph configuration, minimize the addition of ad-hoc postulates and provide a clear interpretation of the ranking of arguments.

Original languageEnglish
Title of host publication17th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2018
PublisherInternational Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
Pages1132-1140
Number of pages9
ISBN (Print)9781510868083
Publication statusPublished - 2018
Event17th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2018 - Stockholm, Sweden
Duration: 10 Jul 201815 Jul 2018

Publication series

NameProceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
Volume2
ISSN (Print)1548-8403
ISSN (Electronic)1558-2914

Conference

Conference17th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2018
Country/TerritorySweden
CityStockholm
Period10/07/1815/07/18

Keywords

  • Abstract argumentation
  • Ranking-based semantics

Fingerprint

Dive into the research topics of 'Ranking semantics based on subgraphs analy'. Together they form a unique fingerprint.

Cite this