Syntax-Based Default Reasoning as Probabilistic Model-Based Diagnosis
Abstract
We view the syntax-based approaches to default reasoning as a model-based diagnosis problem, where each source giving a piece of information is considered as a component. It is formalized in the ATMS framework (each source corresponds to an assumption). We assume then that all sources are independent and "fail" with a very small probability. This leads to a probability assignment on the set of candidates, or equivalently on the set of consistent environments. This probability assignment induces a Dempster-Shafer belief function which measures the probability that a proposition can be deduced from the evidence. This belief function can be used in several different ways to define a non-monotonic consequence relation. We study and compare these consequence relations. The -case of prioritized knowledge bases is briefly considered.
Cite
Text
Lang. "Syntax-Based Default Reasoning as Probabilistic Model-Based Diagnosis." Conference on Uncertainty in Artificial Intelligence, 1994. doi:10.1016/B978-1-55860-332-5.50054-7Markdown
[Lang. "Syntax-Based Default Reasoning as Probabilistic Model-Based Diagnosis." Conference on Uncertainty in Artificial Intelligence, 1994.](https://mlanthology.org/uai/1994/lang1994uai-syntax/) doi:10.1016/B978-1-55860-332-5.50054-7BibTeX
@inproceedings{lang1994uai-syntax,
title = {{Syntax-Based Default Reasoning as Probabilistic Model-Based Diagnosis}},
author = {Lang, Jérôme},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {1994},
pages = {391-398},
doi = {10.1016/B978-1-55860-332-5.50054-7},
url = {https://mlanthology.org/uai/1994/lang1994uai-syntax/}
}