Uncertainty in Artificial Intelligence
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Dialectic Reasoning with Inconsistent Information
Morten Elvang-Gøransson, Paul Krause, John Fox
Abstract:
From an inconsistent database non-trivial arguments may be constructed both for a proposition, and for the contrary of that proposition. Therefore, inconsistency in a logical database causes uncertainty about which conclusions to accept. This kind of uncertainty is called logical uncertainty. We define a concept of "acceptability", which induces a means for differentiating arguments. The more acceptable an argument, the more confident we are in it. A specific interest is to use the acceptability classes to assign linguistic qualifiers to propositions, such that the qualifier assigned to a propositions reflects its logical uncertainty. A more general interest is to understand how classes of acceptability can be defined for arguments constructed from an inconsistent database, and how this notion of acceptability can be devised to reflect different criteria. Whilst concentrating on the aspects of assigning linguistic qualifiers to propositions, we also indicate the more general significance of the notion of acceptability.
Keywords:
Pages: 114-121
PS Link:
PDF Link: /papers/93/p114-elvang-goransson.pdf
BibTex:
@INPROCEEDINGS{Elvang-Gøransson93,
AUTHOR = "Morten Elvang-Gøransson and Paul Krause and John Fox",
TITLE = "Dialectic Reasoning with Inconsistent Information",
BOOKTITLE = "Proceedings of the Ninth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-93)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1993",
PAGES = "114--121"
}


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