English

A Unified framework for order-of-magnitude confidence relations

Artificial Intelligence 2012-08-07 v2

Abstract

The aim of this work is to provide a unified framework for ordinal representations of uncertainty lying at the crosswords between possibility and probability theories. Such confidence relations between events are commonly found in monotonic reasoning, inconsistency management, or qualitative decision theory. They start either from probability theory, making it more qualitative, or from possibility theory, making it more expressive. We show these two trends converge to a class of genuine probability theories. We provide characterization results for these useful tools that preserve the qualitative nature of possibility rankings, while enjoying the power of expressivity of additive representations.

Keywords

Cite

@article{arxiv.1207.4117,
  title  = {A Unified framework for order-of-magnitude confidence relations},
  author = {Didier Dubois and Helene Fargier},
  journal= {arXiv preprint arXiv:1207.4117},
  year   = {2012}
}

Comments

Appears in Proceedings of the Twentieth Conference on Uncertainty in Artificial Intelligence (UAI2004)