Modeling uncertain and vague knowledge in possibility and evidence theories

TL;DR

This paper explores new theories of uncertainty, specifically possibility and evidence theories, to better model vagueness and uncertain knowledge in AI, offering an alternative to traditional probability-based approaches.

Contribution

It introduces and advocates for the use of possibility and evidence theories as effective tools for modeling vagueness in AI, providing a partial response to probabilistic methods.

Findings

Possibility and evidence theories effectively model vagueness in AI.

These theories offer an alternative to probability for uncertain knowledge.

The approach enhances understanding of uncertain and vague information in AI systems.

Abstract

This paper advocates the usefulness of new theories of uncertainty for the purpose of modeling some facets of uncertain knowledge, especially vagueness, in AI. It can be viewed as a partial reply to Cheeseman's (among others) defense of probability.