A declarative semantics for Bousi~Prolog

Julián-Iranzo, Pascual; Rubio-Manzano, Clemente

doi:10.1145/1599410.1599430

Cited by 15 publications

(9 citation statements)

References 23 publications

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“…On the other hand, in [11,10,22] we provided some advances in the design of declarative semantics and/or correctness properties regarding the development of fuzzy logic languages dealing with similarity/proximity relations (Bousi∼Prolog) or highly expressive lattices modeling truth degrees (MALP). As a matter of future work we want to establish that analogous -but reinforced-formal properties also hold in the language FASILL.…”

Section: Discussionmentioning

confidence: 99%

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A Fuzzy Logic Programming Environment for Managing Similarity and Truth Degrees

Julián-Iranzo

Moreno

Penabad

et al. 2015

Electron. Proc. Theor. Comput. Sci.

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FASILL (acronym of "Fuzzy Aggregators and Similarity Into a Logic Language") is a fuzzy logic programming language with implicit/explicit truth degree annotations, a great variety of connectives and unification by similarity. FASILL integrates and extends features coming from MALP (Multi-Adjoint Logic Programming, a fuzzy logic language with explicitly annotated rules) and Bousi∼Prolog (which uses a weak unification algorithm and is well suited for flexible query answering). Hence, it properly manages similarity and truth degrees in a single framework combining the expressive benefits of both languages. This paper presents the main features and implementations details of FASILL. Along the paper we describe its syntax and operational semantics and we give clues of the implementation of the lattice module and the similarity module, two of the main building blocks of the new programming environment which enriches the FLOPER system developed in our research group.

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Section: Discussionmentioning

confidence: 99%

“…Some of them are extended to cope with similarities. Concretely, following the line of Bousi∼Prolog [11], the most general unifier is replaced by the concept of weak most general unifier (w.m.g.u.) and a weak unification algorithm is introduced to compute it.…”

Section: Definition 24 (Program) a Program P Is A Tuple π R L Whementioning

confidence: 99%

A Fuzzy Logic Programming Environment for Managing Similarity and Truth Degrees

Julián-Iranzo

Moreno

Penabad

et al. 2015

Electron. Proc. Theor. Comput. Sci.

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“…However, the concept of (Julian-Iranzo et al 2009, 2010) is similar but not identical with one of our former ideas about evaluating of special fuzzy Datalog programs (Achs 2006). Reading these papers has led to the assumption that this former idea may be the base of a top-down-like evaluation strategy in special multivalued cases as well.…”

Section: Introductionmentioning

confidence: 90%

From Fuzzy Datalog to Multivalued Knowledge-Base

Achs¹

2012

Fuzzy Logic - Algorithms, Techniques and Implementations

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“…As noted by (Shenoi and Melton 1999) and other authors, the transitivity property required for similarity relations may conflict with user's intentions in some cases. The Bousi∼Prolog language (Julián-Iranzo et al 2009;Julián-Iranzo and Rubio-Manzano 2009b;Julián-Iranzo and Rubio-Manzano 2009a) has been designed with the aim of generalizing SLP to work with proximity relations. A different generalization of SLP is the SQLP scheme (Caballero et al 2008), designed as an extension of the QLP scheme.…”

Section: Introductionmentioning

confidence: 99%

A declarative semantics for CLP with qualification and proximity

Rodríguez-Artalejo¹,

Romero-Díaz²

2010

Theory and Practice of Logic Programming

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Uncertainty in Logic Programming has been investigated during the last decades, dealing with various extensions of the classical LP paradigm and different applications. Existing proposals rely on different approaches, such as clause annotations based on uncertain truth values, qualification values as a generalization of uncertain truth values, and unification based on proximity relations. On the other hand, the CLP scheme has established itself as a powerful extension of LP that supports efficient computation over specialized domains while keeping a clean declarative semantics. In this report we propose a new scheme SQ-CLP designed as an extension of CLP that supports qualification values and proximity relations. We show that several previous proposals can be viewed as particular cases of the new scheme, obtained by partial instantiation. We present a declarative semantics for SQCLP that is based on observables, providing fixpoint and proof-theoretical characterizations of least program models as well as an implementation-independent notion of goal solutions.

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A declarative semantics for Bousi~Prolog

Cited by 15 publications

References 23 publications

A Fuzzy Logic Programming Environment for Managing Similarity and Truth Degrees

A Fuzzy Logic Programming Environment for Managing Similarity and Truth Degrees

From Fuzzy Datalog to Multivalued Knowledge-Base

A declarative semantics for CLP with qualification and proximity

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