Uncertainty in Artificial Intelligence
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Resolving Conflicting Arguments under Uncertainties
Benson Ng, Kam-Fai Wong, Boon-Toh Low
Abstract:
Distributed knowledge based applications in open domain rely on common sense information which is bound to be uncertain and incomplete. To draw the useful conclusions from ambiguous data, one must address uncertainties and conflicts incurred in a holistic view. No integrated frameworks are viable without an in-depth analysis of conflicts incurred by uncertainties. In this paper, we give such an analysis and based on the result, propose an integrated framework. Our framework extends definite argumentation theory to model uncertainty. It supports three views over conflicting and uncertain knowledge. Thus, knowledge engineers can draw different conclusions depending on the application context (i.e. view). We also give an illustrative example on strategical decision support to show the practical usefulness of our framework.
Keywords: Uncertainy, disjunction, argumentation, distributed reasoning.
Pages: 414-421
PS Link:
PDF Link: /papers/98/p414-ng.pdf
BibTex:
@INPROCEEDINGS{Ng98,
AUTHOR = "Benson Ng and Kam-Fai Wong and Boon-Toh Low",
TITLE = "Resolving Conflicting Arguments under Uncertainties",
BOOKTITLE = "Proceedings of the Fourteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-98)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1998",
PAGES = "414--421"
}


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