Probabilistic Acceptance
Artificial Intelligence
2013-02-08 v1
Abstract
The idea of fully accepting statements when the evidence has rendered them probable enough faces a number of difficulties. We leave the interpretation of probability largely open, but attempt to suggest a contextual approach to full belief. We show that the difficulties of probabilistic acceptance are not as severe as they are sometimes painted, and that though there are oddities associated with probabilistic acceptance they are in some instances less awkward than the difficulties associated with other nonmonotonic formalisms. We show that the structure at which we arrive provides a natural home for statistical inference.
Cite
@article{arxiv.1302.1556,
title = {Probabilistic Acceptance},
author = {Henry E. Kyburg},
journal= {arXiv preprint arXiv:1302.1556},
year = {2013}
}
Comments
Appears in Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence (UAI1997)