Titlebook: Symbolic and Quantitative Approaches to Reasoning and Uncertainty; European Conference Michael Clarke,Rudolf Kruse,Serafín Moral Conferenc

得罪人

Lecture Notes in Computer Sciencehttp://image.papertrans.cn/t/image/883842.jpg

facetious

https://doi.org/10.1007/BFb0028174Bayesian network; Dynamic Reasoning; Dynamisches Schlie?en; Fuzzy Mengen; Fuzzy Sets; M?glichkeitstheorie

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Ardent

Learning non probabilistic belief networks,asic concepts as marginalization, conditioning and independence for probability intervals. Then we develop some algorithms for learning simple belief networks (trees and polytrees), based on this kind of non purely probabilistic information.

FAST

Adenocarcinoma

cumber

Taxonomic linear theories,sent these networks. In order to compare our inferences, we have chosen one nonmonotonic logic: default logic [9] serves as a reference. The main result proves the equivalence between linear logic and default logic in .. We hope this will help to better understand the relations between nonmonotonicity and defeasible knowledge representation.

seroma

Possibilistic logic: From nonmonotonicity to logic programming,Links between preferential semantics of possibilistic logic, semantics of prioritized circumscription, and one of the semantics used in logic programming, namely the perfect model semantics of stratified logic programs, are presented.

AWL

The use of possibilistic logic PL1 in a customizable tool for the generation of production-rule basA theoretical framework is proposed, in which possibilistic logic can be uniformly used to treat uncertainty associated with production rules, when knowledge is represented in either logic or frames. This model is being implemented in the uncertainty module of a tool designed to allow the construction of expert system shells.

顯而易見

,Acceptability of arguments as ‘logical uncertainty’,We briefly discuss the argumentation-paradigm as a unifying framework for practical reasoning and then turn to the definition of a system of argumentation that induces a novel concept of logical uncertainty over conclusions drawn from inconsistent databases.