Extending PSL with Fuzzy Quantifiers
نویسندگان
چکیده
Golnoosh Farnadi, Stephen H. Bach, Marie-Francine Moens, Lise Getoor, Martine De Cock Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Belgium, Department of Computer Science, Katholieke Universiteit Leuven, Belgium, Statistical Relational Learning Group, University of Maryland, USA, University of California, Santa Cruz, USA, Center for Data Science, University of Washington Tacoma, USA
منابع مشابه
COMBINING FUZZY QUANTIFIERS AND NEAT OPERATORS FOR SOFT COMPUTING
This paper will introduce a new method to obtain the order weightsof the Ordered Weighted Averaging (OWA) operator. We will first show therelation between fuzzy quantifiers and neat OWA operators and then offer anew combination of them. Fuzzy quantifiers are applied for soft computingin modeling the optimism degree of the decision maker. In using neat operators,the ordering of the inputs is not...
متن کاملFuzzy Quantifiers, Multiple Variable Binding and Branching Quantification
Lindström [1] introduced a very powerful notion of quantifiers, which permits multi-place quantification and the simultaneous binding of several variables. ‘Branching’ quantifification was found to be useful by linguists e.g. for modelling reciprocal constructions like “Most men and most women admire each other”. Westerståhl [2] showed how to compute the three-place Lindström quantifier for “Q1...
متن کاملStatistical Relational Learning with Soft Quantifiers
Quantification in statistical relational learning (SRL) is either existential or universal, however humans might be more inclined to express knowledge using soft quantifiers, such as “most” and “a few”. In this paper, we define the syntax and semantics of PSL, a new SRL framework that supports reasoning with soft quantifiers, and present its most probable explanation (MPE) inference algorithm. ...
متن کاملOn the Semantic Relationship between Probabilistic Soft Logic and Markov Logic
Markov Logic Networks (MLN) and Probabilistic Soft Logic (PSL) are widely applied formalisms in Statistical Relational Learning, an emerging area in Artificial Intelligence that is concerned with combining logical and statistical AI. Despite their resemblance, the relationship has not been formally stated. In this paper, we describe the precise semantic relationship between them from a logical ...
متن کاملBasic Properties of L-fuzzy Quantifiers of the Type <1> Determined by Fuzzy Measures
The aim of this paper is to study monadic L-fuzzy quantifiers of the type 〈1〉 determined by fuzzy measures. These fuzzy quantifiers are defined using a novel notion of ⊗-fuzzy integral. Several semantic properties of these L-fuzzy quantifiers are studied. Keywords— fuzzy integral, fuzzy logic, fuzzy measure, fuzzy quantifier, generalized quantifier
متن کامل