Syntaxbased Default Reasoning as Probabilistic Modelbased Diagnosis
Jerome Lang
Abstract:
We view the syntaxbased approaches to default reasoning as a modelbased 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 DempsterShafer 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 nonmonotonic consequence relation. We study and compare these consequence relations. The case of prioritized knowledge bases is briefly considered.
Keywords:
Pages: 391398
PS Link:
PDF Link: /papers/94/p391lang.pdf
BibTex:
@INPROCEEDINGS{Lang94,
AUTHOR = "Jerome Lang
",
TITLE = "Syntaxbased Default Reasoning as Probabilistic Modelbased Diagnosis",
BOOKTITLE = "Proceedings of the Tenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI94)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1994",
PAGES = "391398"
}

