Uncertainty in Artificial Intelligence
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On the Semantics and Automated Deduction for PLFC, a Logic of Possibilistic Uncertainty and Fuzziness
Teresa Alsinet, Lluis Godo, Sandra Sandri
Possibilistic logic is a well-known graded logic of uncertainty suitable to reason under incomplete information and partially inconsistent knowledge, which is built upon classical first order logic. There exists for Possibilistic logic a proof procedure based on a refutation complete resolution-style calculus. Recently, a syntactical extension of first order Possibilistic logic (called PLFC) dealing with fuzzy constants and fuzzily restricted quantifiers has been proposed. Our aim is to present steps towards both the formalization of PLFC itself and an automated deduction system for it by (i) providing a formal semantics; (ii) defining a sound resolution-style calculus by refutation; and (iii) describing a first-order proof procedure for PLFC clauses based on (ii) and on a novel notion of most general substitution of two literals in a resolution step. In contrast to standard Possibilistic logic semantics, truth-evaluation of formulas with fuzzy constants are many-valued instead of boolean, and consequently an extended notion of possibilistic uncertainty is also needed.
Keywords: Possibilistic logic, fuzzy constants, fuzzily restricted quantifiers
Pages: 3-12
PS Link: http://fermat.eup.udl.es/~tracy/uai99.ps
PDF Link: /papers/99/p3-alsinet.pdf
AUTHOR = "Teresa Alsinet and Lluis Godo and Sandra Sandri",
TITLE = "On the Semantics and Automated Deduction for PLFC, a Logic of Possibilistic Uncertainty and Fuzziness",
BOOKTITLE = "Proceedings of the Fifteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-99)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1999",
PAGES = "3--12"

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