Extending Fuzzy Linguistic Logic Programming with Negation

Fuzzy linguistic logic programming (FLLP) is a framework for representation and reasoning with linguistically expressed human knowledge. In this paper, we extend FLLP by allowing negative literals to appear in rule bodies, resulting in normal logic programs. We study the stable model semantics and w...

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Vydané v:Mathematics (Basel) Ročník 10; číslo 17; s. 3105
Hlavný autor: Le, Van Hung
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 01.09.2022
Predmet:
Computer programming
Domains
Fuzzy algorithms
Fuzzy logic
Fuzzy systems
Innovations
Knowledge
Linguistics
Literature reviews
Logic
Logic programming
Logic programs
Mathematics
Methods
Negation
Negation (Linguistics)
Property
Queries
Semantics
Transformation
Truth
Vietnam
ISSN:2227-7390, 2227-7390
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Abstract Fuzzy linguistic logic programming (FLLP) is a framework for representation and reasoning with linguistically expressed human knowledge. In this paper, we extend FLLP by allowing negative literals to appear in rule bodies, resulting in normal logic programs. We study the stable model semantics and well-founded semantics of such programs and their relation. The two kinds of semantics are adapted from those of classical ones based on the Gelfond–Lifschitz transformation and van Gelder’s alternating fixpoint approach, respectively. To our knowledge, until now, there has been no work on the well-founded semantics of normal programs in any fuzzy logic programming (FLP) framework based on Vojtáš’s FLP. Moreover, the relation between the two kinds of semantics is usually studied using a bilattice setting of the truth domain. However, our truth domains do not possess a complete knowledge-ordering lattice and, thus, do not have a bilattice structure. The two kinds of semantics possess properties similar to those of the classical case. Every stable model contains the well-founded (partial) model, and the well-founded total model coincides with the unique stable model, but not vice versa. Since the well-founded semantics is closely related to the stable model semantics, it can help compute stable models more efficiently.
AbstractList Fuzzy linguistic logic programming (FLLP) is a framework for representation and reasoning with linguistically expressed human knowledge. In this paper, we extend FLLP by allowing negative literals to appear in rule bodies, resulting in normal logic programs. We study the stable model semantics and well-founded semantics of such programs and their relation. The two kinds of semantics are adapted from those of classical ones based on the Gelfond–Lifschitz transformation and van Gelder’s alternating fixpoint approach, respectively. To our knowledge, until now, there has been no work on the well-founded semantics of normal programs in any fuzzy logic programming (FLP) framework based on Vojtáš’s FLP. Moreover, the relation between the two kinds of semantics is usually studied using a bilattice setting of the truth domain. However, our truth domains do not possess a complete knowledge-ordering lattice and, thus, do not have a bilattice structure. The two kinds of semantics possess properties similar to those of the classical case. Every stable model contains the well-founded (partial) model, and the well-founded total model coincides with the unique stable model, but not vice versa. Since the well-founded semantics is closely related to the stable model semantics, it can help compute stable models more efficiently.
Audience Academic
Author Le, Van Hung
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Snippet Fuzzy linguistic logic programming (FLLP) is a framework for representation and reasoning with linguistically expressed human knowledge. In this paper, we...
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SubjectTerms Computer programming
Domains
Fuzzy algorithms
Fuzzy logic
Fuzzy systems
Innovations
Knowledge
Linguistics
Literature reviews
Logic
Logic programming
Logic programs
Mathematics
Methods
Negation
Negation (Linguistics)
Property
Queries
Semantics
Transformation
Truth
Title Extending Fuzzy Linguistic Logic Programming with Negation
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