Research Note on Uncertain Probabilities and Abstract Argumentation

TL;DR

This paper introduces a formal framework for incorporating uncertain probabilities and confidence levels into abstract argumentation, leveraging Sato's distribution semantics to improve probabilistic inference in argumentation systems.

Contribution

It proposes a novel approach that integrates degrees of belief and confidence into argumentation, extending probabilistic argumentation with a focus on inference using Sato's semantics.

Findings

Framework for uncertain probabilities in argumentation

Connections with existing probabilistic argumentation models

Discussion on practical inference with uncertain data

Abstract

The sixth assessment of the international panel on climate change (IPCC) states that "cumulative net CO2 emissions over the last decade (2010-2019) are about the same size as the 11 remaining carbon budget likely to limit warming to 1.5C (medium confidence)." Such reports directly feed the public discourse, but nuances such as the degree of belief and of confidence are often lost. In this paper, we propose a formal account for allowing such degrees of belief and the associated confidence to be used to label arguments in abstract argumentation settings. Differently from other proposals in probabilistic argumentation, we focus on the task of probabilistic inference over a chosen query building upon Sato's distribution semantics which has been already shown to encompass a variety of cases including the semantics of Bayesian networks. Borrowing from the vast literature on such semantics, we examine how such tasks can be dealt with in practice when considering uncertain probabilities, and discuss the connections with existing proposals for probabilistic argumentation.