Learning argumentation frameworks from labelings

Lars Bengel*, Matthias Thimm, Tjitze Rienstra

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

We consider the problem of learning argumentation frameworks from a given set of labelings such that every input is a s-labeling of these argumentation frameworks. Our new algorithm takes labelings and computes attack constraints for each argument that represent the restrictions on argumentation frameworks that are consistent with the input labelings. Having constraints on the level of arguments allows for a very effective parallelization of all computations. An important element of this approach is maintaining a representation of all argumentation frameworks that satisfy the input labelings instead of simply finding any suitable argumentation framework. This is especially important, for example, if we receive additional labelings at a later time and want to refine our result without having to start all over again. The developed algorithm is compared to previous works and an evaluation of its performance has been conducted.
Original languageEnglish
Pages (from-to)121-159
Number of pages39
JournalArgument and Computation
Volume15
Issue number2
DOIs
Publication statusPublished - 12 Jul 2024

Keywords

  • argumentation
  • labelings
  • learning
  • semantics

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