A Similarity-Based WAM for Bousi~Prolog

Julián-Iranzo, Pascual; Rubio-Manzano, Clemente

doi:10.1007/978-3-642-02478-8_31

Cited by 15 publications

(7 citation statements)

References 5 publications

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“…Firstly, the definition of the syntax; secondly, the elaboration of a formal study of its semantics; and thirdly, an implementation of the system. In order to address the tasks related with syntax and semantics, we will follow the guidelines established in [19] and [20] 1 ; for the implementation task, we will follow the guidelines detailed in [16]. Example 3.…”

Section: Simple Interval-valued Fuzzy Prolog: Syntax Semantics and Im...mentioning

confidence: 99%

“…This task involves different challenges both from theoretical and implementation points of view. The former entails adding a IVFSs arithmetic into the Warren Abstract Machine based on Similarity (SWAM) [16]; the latter, means to establish a (model-theoretic) declarative semantics for the language in the classical way, formalising the notion of least interval valued fuzzy Herbrand model for interval-valued fuzzy definite programs. This paper is divided into the following sections: section 2 introduces the concepts that support our approach; section 3 describes the details of the syntax, semantics and implementation of the proposed language; section 4 analyses different realms where this programming language can be applied; in section 5, the main differences between this proposal an others that are described in the literature are discussed; and, finally, section 6 summarizes our main conclusions and some ideas for future work.…”

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confidence: 99%

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On the Incorporation of Interval-Valued Fuzzy Sets into the Bousi-Prolog System: Declarative Semantics, Implementation and Applications

Rubio-Manzano

Pereira-Fariña

2018

Studies in Computational Intelligence

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In this paper we analyse the benefits of incorporating interval-valued fuzzy sets into the Bousi-Prolog system. A syntax, declarative semantics and implementation for this extension is presented and formalised. We show, by using potential applications, that fuzzy logic programming frameworks enhanced with them can correctly work together with lexical resources and ontologies in order to improve their capabilities for knowledge representation and reasoning.

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Section: Simple Interval-valued Fuzzy Prolog: Syntax Semantics and Im...mentioning

confidence: 99%

mentioning

confidence: 99%

On the Incorporation of Interval-Valued Fuzzy Sets into the Bousi-Prolog System: Declarative Semantics, Implementation and Applications

Rubio-Manzano

Pereira-Fariña

2018

Studies in Computational Intelligence

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“…Neither LIKELOG nor SiLog are publicly available, what prevent a real evaluation of these systems, and they seem immature prototypes. In this same line of work, Bousi∼Prolog [12,15], on the other hand, is the first fuzzy logic programming system which is a true PROLOG extension and not a simple interpreter able to execute a weak SLDresolution procedure. Also it is the first fuzzy logic programming language that proposed the use of proximity relations as a generalization of similarity relations [11].…”

Section: Introductionmentioning

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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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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“…SLPlike languages using proximity relations include Bousi∼Prolog (Julián-Iranzo and Rubio-Manzano 2009a) and the SQCLP scheme (Rodríguez-Artalejo and Romero-Díaz 2010a). Two prototype implementations of Bousi∼Prolog are available: a low-level implementation (Julián-Iranzo and Rubio-Manzano 2009b) based on an adaptation of the classical Warren Abstract Machine (WAM) (called Similarity WAM) implemented in Java and able to execute a Prolog program in the context of a similarity relation defined on the first-order alphabet induced by that program; and a high-level implementation (Julián-Iranzo et al 2009) done on top of SWI-Prolog by means of a program transformation from Bousi∼Prolog programs into the so-called Translated BPL code than can be executed according to the weak SLD resolution principle by a meta-interpreter.…”

Section: Introductionmentioning

confidence: 99%

A Transformation-based implementation for CLP with qualification and proximity

Caballero¹,

Rodríguez-Artalejo²,

Romero-Díaz³

2012

Theory and Practice of Logic Programming

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Uncertainty in logic programming has been widely investigated in the last decades, leading to multiple extensions of the classical LP paradigm. However, few of these are designed as extensions of the well-established and powerful CLP scheme for Constraint Logic Programming. In a previous work we have proposed the SQCLP (proximity-based qualified constraint logic programming) scheme as a quite expressive extension of CLP with support for qualification values and proximity relations as generalizations of uncertainty values and similarity relations, respectively. In this paper we provide a transformation technique for transforming SQCLP programs and goals into semantically equivalent CLP programs and goals, and a practical Prolog-based implementation of some particularly useful instances of the SQCLP scheme. We also illustrate, by showing some simple-and working-examples, how the prototype can be effectively used as a tool for solving problems where qualification values and proximity relations play a key role. Intended use of SQCLP includes flexible information retrieval applications. Definition 2.3 (Correct Abstract Goal Solving Systems)An abstract goal solving system for SQCLP(S, D, C) is any device that takes a program P and a goal G as input and yields various triples σ, µ, Π , called computed answers, as outputs. Such a goal solving system is called:1. Sound iff every computed answer is a solution σ, µ, Π ∈ Sol P (G).

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A Similarity-Based WAM for Bousi~Prolog

Cited by 15 publications

References 5 publications

On the Incorporation of Interval-Valued Fuzzy Sets into the Bousi-Prolog System: Declarative Semantics, Implementation and Applications

On the Incorporation of Interval-Valued Fuzzy Sets into the Bousi-Prolog System: Declarative Semantics, Implementation and Applications

A Fuzzy Logic Programming Environment for Managing Similarity and Truth Degrees

A Transformation-based implementation for CLP with qualification and proximity

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