RFuzzy: Syntax, semantics and implementation details of a simple and expressive fuzzy tool over Prolog

Muñoz-Hernández, Susana; Pablos-Ceruelo, Víctor; Straß, Hannes

doi:10.1016/j.ins.2010.07.033

Cited by 26 publications

(14 citation statements)

References 36 publications

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“…Fuzzy logic languages can be classified (among other criteria) regarding the emphasis they assign when fuzzifying the original unification/resolution mechanisms of PROLOG. So, whereas some approaches are able to cope with similarity/proximity relations at unification time [9,1,29], other ones extend their operational principles (maintaining syntactic unification) for managing a wide variety of fuzzy connectives and truth degrees on rules/goals beyond the simpler case of true or false [16,19,24].…”

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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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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“…To overcome this situation, during the last decades several fuzzy logic programming systems have been developed where the classical inference mechanism of SLD-resolution is replaced with a fuzzy variant able to handle partial truth and to reason with uncertainty, thus promoting the development of real-world applications in the fields of artificial/computational intelligence, soft-computing, semantic web, etc. Most of these systems implement the fuzzy resolution principle introduced by Lee in [2], such as languages Prolog-Elf [3], F-Prolog [4], Fril [5], (S-)QLP [6] [7], RFuzzy [8] and MALP [9], being this last approach our target goal.…”

Section: Introductionmentioning

confidence: 99%

“…of intervals between 0 and 1[8] [26]) as a Prolog program for being used into  as follows:  A member of this algebra is a list of pairs representing disjoined intervals. The top element is the point 1 (interval from 1 to 1), and the bottom one is the point 0 (interval from 0 to 0).  A union of intervals, U , is less or equal than other union of intervals U ′ if for each I U ∈ , there exists another interval I U ′ ′ ∈ , such that I I′ ⊆ .…”

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confidence: 99%

Fuzzy Logic Programming in Action with <i>FLOPER</i>

Moreno¹,

Vázquez²

2014

JSEA

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During the last years, we have developed the  platform for providing a practical support to the so-called Multi-Adjoint Logic Programming approach (MALP in brief), which represents an extremely flexible framework into the Fuzzy Logic Programming arena. Nowadays,  is useful for compiling (to standard Prolog code), executing and debugging (by drawing execution trees) MALP programs, and it is ready for being extended in the near future with powerful transformation and optimization techniques designed in our research group during the recent past. Our last update consists in the integration of a graphical interface for a comfortable interaction with the system which allows, among other capabilities, the use of projects for packing scripts and auxiliary definitions of fuzzy sets/connectives, together with fuzzy programs and their associated lattices modeling truth-degrees beyond the simpler crisp case { ; } true false .

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“…Leaving apart the theoretical frameworks, as [4], we know about the Prolog-Elf system [5], the FRIL Prolog system [6], the F-Prolog language [7], the FuzzyDL reasoner [8], the Fuzzy Logic Programming Environment for Research (FLOPER) [9], the Fuzzy Prolog system [10], [11], or Rfuzzy [12]. All of them implement in some way the fuzzy set theory introduced by Lotfi Zadeh in 1965 ( [13]), and all of them let you implement the connectors needed to retrieve the non-fuzzy information stored in databases, but we needed more meta-information than the one they provide.…”

Section: Introductionmentioning

confidence: 99%

On modelling real-world knowledge to get answers to fuzzy and flexible searches without human intervention

Pablos-Ceruelo

Muñoz-Hernández

2014

2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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The Internet has become a place where massive amounts of information and data are being generated every day. This information is most of the times stored in a nonstructured way, but the times it is structured in databases it cannot be retrieved by using easy fuzzy queries. Being the information in the database the distance to the city center of some restaurants (and their names) by easy fuzzy queries we mean queries like "I want a restaurant close to the center". Since the computer does not have knowledge about the relation between being close to the center and the distance to the center (of a restaurant) it does not know how to answer this query by itself. We need human intervention to tell the computer from which database column it needs to retrieve data (the one with the restaurant's distance to the center), and how this nonfuzzy information is fuzzified (applying the close function to the retrieved value). Once this is done it can give an answer, just ordering the database elements by this new computed attribute. This example is very simple, but there are others not so simple, as "I want a restaurant close to the center, not very expensive and whose food type is mediterranean". Doing this for each existing attribute does not seem to be a very good idea. We present a web interface for posing fuzzy and flexible queries and a search engine capable of answering them without human intervention, just from the knowledge modelled by using the framework's syntax. We expect this work contributes to the development of more human-oriented fuzzy search engines.

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RFuzzy: Syntax, semantics and implementation details of a simple and expressive fuzzy tool over Prolog

Cited by 26 publications

References 36 publications

A Fuzzy Logic Programming Environment for Managing Similarity and Truth Degrees

A Fuzzy Logic Programming Environment for Managing Similarity and Truth Degrees

Fuzzy Logic Programming in Action with <i>FLOPER</i>

On modelling real-world knowledge to get answers to fuzzy and flexible searches without human intervention

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RFuzzy: Syntax, semantics and implementation details of a simple and expressive fuzzy tool over Prolog

Cited by 26 publications

References 36 publications

A Fuzzy Logic Programming Environment for Managing Similarity and Truth Degrees

A Fuzzy Logic Programming Environment for Managing Similarity and Truth Degrees

Fuzzy Logic Programming in Action with &lt;i&gt;FLOPER&lt;/i&gt;

On modelling real-world knowledge to get answers to fuzzy and flexible searches without human intervention

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Product

Resources

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Fuzzy Logic Programming in Action with <i>FLOPER</i>