Ranking Transition-based Medical Recommendations using Assumption-based Argumentation

Kenneth Skiba, Matthias Thimm, Johannes Peter Wallner

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

Abstract

We present a general framework to rank assumption in assumption-based argumentation frameworks (ABA frameworks), relying on their relationship to other assumptions and the syntactical structure of the ABA framework. We propose a new family of semantics for ABA frameworks that is using reductions to the abstract argumentation setting and leveraging existing ranking-based semantics for abstract argumentation. We show the suitability of these semantics by investigating a case study based on medical recommendations for patients with multiple health conditions and show that the relationship of the recommendations are enough to establish a ranking between the recommendations.
Original languageEnglish
Title of host publicationRobust Argumentation Machines - First International Conference, RATIO 2024, Proceedings
EditorsPhilipp Cimiano, Anette Frank, Michael Kohlhase, Benno Stein
PublisherSpringer
Pages202-220
Number of pages19
ISBN (Print)9783031635359
DOIs
Publication statusPublished - 2024
Event1st International Conference on Recent Advances in Robust Argumentation Machines: RATIO 2024 - Bielefeld, Germany
Duration: 5 Jun 20247 Jun 2024
https://ratio-conference.net/

Publication series

NameLecture Notes in Computer Science
Volume14638

Conference

Conference1st International Conference on Recent Advances in Robust Argumentation Machines
Abbreviated titleRATIO 2024
Country/TerritoryGermany
CityBielefeld
Period5/06/247/06/24
Internet address

Keywords

  • ABA
  • Ranking-based semantics
  • TMR

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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