Argumentation as a General Framework for Uncertain Reasoning

TL;DR

This paper presents a unified argumentation framework for uncertain reasoning, modeling arguments with labels for grounds and confidence, and demonstrating its applicability across various uncertainty calculi.

Contribution

It introduces a generalized argumentation system based on minimal logic, enabling aggregation of arguments and unifying different uncertainty methods.

Findings

Provides a formal model for argument-based uncertain reasoning

Shows how various uncertainty calculi can be represented within this framework

Demonstrates aggregation of arguments using numeric and symbolic functions

Abstract

Argumentation is the process of constructing arguments about propositions, and the assignment of statements of confidence to those propositions based on the nature and relative strength of their supporting arguments. The process is modelled as a labelled deductive system, in which propositions are doubly labelled with the grounds on which they are based and a representation of the confidence attached to the argument. Argument construction is captured by a generalized argument consequence relation based on the ^,--fragment of minimal logic. Arguments can be aggregated by a variety of numeric and symbolic flattening functions. This approach appears to shed light on the common logical structure of a variety of quantitative, qualitative and defeasible uncertainty calculi.