Belief Functions and Default Reasoning
Artificial Intelligence
2013-02-21 v1
Abstract
We present a new approach to dealing with default information based on the theory of belief functions. Our semantic structures, inspired by Adams' epsilon-semantics, are epsilon-belief assignments, where values committed to focal elements are either close to 0 or close to 1. We define two systems based on these structures, and relate them to other non-monotonic systems presented in the literature. We show that our second system correctly addresses the well-known problems of specificity, irrelevance, blocking of inheritance, ambiguity, and redundancy.
Keywords
Cite
@article{arxiv.1302.4930,
title = {Belief Functions and Default Reasoning},
author = {Salem Benferhat and Alessandro Saffiotti and Philippe Smets},
journal= {arXiv preprint arXiv:1302.4930},
year = {2013}
}
Comments
Appears in Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence (UAI1995)