---
title: "Bounding Acceptability Degrees and Eliciting Initial Weights in Gradual Argumentation"
authors:
  - "Nir Oren"
  - "Bruno Yun"
doi: "10.24963/ijcai.2026/443"
url: "https://ijcai.org/proceedings/2026/443"
pdf: "https://ijcai.org/proceedings/2026/0443.pdf"
venue: "Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence"
year: 2026
pages: "3979-3987"
track: "Main Track"
keywords:
  - "Knowledge Representation and Reasoning: Argumentation"
---

# Bounding Acceptability Degrees and Eliciting Initial Weights in Gradual Argumentation

Nir Oren, Bruno Yun

*Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence*, pages 3979-3987. DOI: [10.24963/ijcai.2026/443](https://doi.org/10.24963/ijcai.2026/443)

## Abstract

Many semantics for abstract weighted argumentation assume that each argument is associated with a numerical initial weight. Eliciting these initial weights poses several challenges: (1) accurately providing a specific numerical value is often difficult, and (2) individuals frequently confuse initial weights with acceptability degrees in the presence of other arguments. We therefore propose an elicitation pipeline that allows a user to specify their believed final acceptability degree intervals for each argument. We can determine which portion (if any) of these intervals are rational, refining the intervals, or restoring rationality when the intervals are irrational. This allows us to ultimately identify possible initial weights for each argument.