Fixpoint Semantics and Completeness of the Computational Model for Fuzzy Linguistic Logic Programming

2008 ◽  
pp. 420-429 ◽  
Author(s):  
Van Hung Le ◽  
Fei Liu ◽  
Dinh Khang Tran
Keyword(s):  
Computational Model ◽  
Logic Programming ◽  
Fixpoint Semantics ◽  
Fuzzy Linguistic

NESTED GUARDED HORN CLAUSES

1990 ◽  
Vol 01 (03) ◽  
pp. 249-263 ◽  
Author(s):  
MORENO FALASCHI ◽  
MAURIZIO GABBRIELLI ◽  
GIORGIO LEVI ◽  
MASAKI MURAKAMI
Keyword(s):  
Logic Programming ◽  
Operational Semantics ◽  
Horn Clauses ◽  
Fixpoint Semantics ◽  
New Feature ◽  
Definition Of

This paper defines a new concurrent logic language, Nested Guarded Horn Clauses (NGHC). The main new feature of the language is its concept of guard. In fact, an NGHC clause has several layers of (standard) guards. This syntactic innovation allows the definition of a complete (i.e. always applicable) set of unfolding rules and therefore of an unfolding semantics which is equivalent, with respect to the success set, to the operational semantics. A fixpoint semantics is also defined in the classic logic programming style and is proved equivalent to the unfolding one. Since it is possible to embed Flat GHC into NGHC, our method can be used to give a fixpoint semantics to FGHC as well.

scholarly journals A Denotational Semantics for Logic Programming

1985 ◽  
Vol 14 (201) ◽  
Author(s):  
Gudmund Skovbjerg Frandsen
Keyword(s):  
Logic Programming ◽  
Logic Program ◽  
Denotational Semantics ◽  
Power Domain ◽  
Fixpoint Semantics ◽  
Standard Domain

A fully abstract denotational semantics for logic programming has not been constructed yet. In this paper we present a denotational semantics that is almost fully abstract. We take the meaning of a logic program to be an element in a Plotkin power domain of substitutions. In this way our result shows that standard domain constructions suffice, when giving a semantics for logic programming. Using the well-known fixpoint semantics of logic programming we have to consider two different fixpoints in order to obtain information about both successful and failed computations. In contrast, our semantics is uniform in that the (single) meaning of a logic program contains information about both successful, failed and infinite computations. Finally, based on the full abstractness result, we argue that the detail level of substitutions is needed in any denotational semantics for logic programming.

scholarly journals Fuzzy linguistic logic programming and its applications

2009 ◽  
Vol 9 (3) ◽  
pp. 309-341 ◽  
Author(s):  
VAN HUNG LE ◽  
FEI LIU ◽  
DINH KHANG TRAN
Keyword(s):  
Logic Programming ◽  
Logic Programs ◽  
Linguistic Terms ◽  
Natural Languages ◽  
Procedural Semantics ◽  
Declarative Semantics ◽  
Linguistic Hedges ◽  
Fuzzy Linguistic ◽  
Semantics Of Logic Programs ◽  
Different Levels

AbstractThe paper introduces fuzzy linguistic logic programming, which is a combination of fuzzy logic programming, introduced by P. Vojtáš, and hedge algebras in order to facilitate the representation and reasoning on human knowledge expressed in natural languages. In fuzzy linguistic logic programming, truth values are linguistic ones, e.g., VeryTrue, VeryProbablyTrue and LittleFalse, taken from a hedge algebra of a linguistic truth variable, and linguistic hedges (modifiers) can be used as unary connectives in formulae. This is motivated by the fact that humans reason mostly in terms of linguistic terms rather than in terms of numbers, and linguistic hedges are often used in natural languages to express different levels of emphasis. The paper presents: (a) the language of fuzzy linguistic logic programming; (b) a declarative semantics in terms of Herbrand interpretations and models; (c) a procedural semantics which directly manipulates linguistic terms to compute a lower bound to the truth value of a query, and proves its soundness; (d) a fixpoint semantics of logic programs, and based on it, proves the completeness of the procedural semantics; (e) several applications of fuzzy linguistic logic programming; and (f) an idea of implementing a system to execute fuzzy linguistic logic programs.

THE 2-TUPLE LINGUISTIC COMPUTATIONAL MODEL: ADVANTAGES OF ITS LINGUISTIC DESCRIPTION, ACCURACY AND CONSISTENCY

2001 ◽  
Vol 09 (supp01) ◽  
pp. 33-48 ◽  
Author(s):  
F. HERRERA ◽  
L. MARTINEZ
Keyword(s):  
Computational Model ◽  
Multicriteria Decision Making ◽  
Extension Principle ◽  
Computing With Words ◽  
Linguistic Context ◽  
Linguistic Description ◽  
Decision Making Problem ◽  
Linguistic Approach ◽  
Fuzzy Linguistic ◽  
Computational Processes

The Fuzzy Linguistic Approach has been applied successfully to different areas. The use of linguistic information for modelling expert preferences implies the use of processes of Computing with Words. To accomplish these processes different approaches has been proposed in the literature: (i) Computational model based on the Extension Principle, (ii) the symbolic one(also called ordinal approach), and (iii) the 2-tuple linguistic computational model. The main problem of the classical approaches, (i) and (ii), is the loss of information and lack of precision during the computational processes. In this paper, we want to compare the linguistic description, accuracy and consistency of the results obtained using each model over the rest ones. To do so, we shall solve a Multiexpert Multicriteria Decision-Making problem defined in a multigranularity linguistic context using the different computational approaches. This comparison helps us to decide what model is more adequated for computing with words.

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