A Transformation-based implementation for CLP with qualification and proximity

Abstract

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).

A Fuzzy Logic Programming Environment for Managing Similarity and Truth Degrees

“…Ending this section, it is worthy to say that our approach differs from the one presented in [6] since they employ a combination of transformation techniques to first extract the definition of a predicate "∼", simulating weak unification in terms of a set of complex program rules that extends the original program. Finally, this predicate "∼" is reduced to a built-in proximity/similarity unification operator (in this case not implemented by rules and very close to the implementation of our weak unification algorithm) that highly improves the efficiency of their previous programming systems.…”

“…During the last years we have developed the FLOPER tool, initially intended for manipulating MALP programs 6 . In its current development state, FLOPER has been equipped with new features in order to cope with more expressive languages and, in particular, with FASILL.…”

“…Ending this section, it is important to say that our research group has been involved both on the development of similarity-based logic programming systems and those that extend the resolution principle, as reveals the design of the Bousi∼Prolog language 1 [11,13,28], where clauses cohabit with similarity/proximity equations, and the development of the FLOPER system 2 , which manages fuzzy programs composed by rules richer than clauses [20,23]. Our unifying approach is somehow inspired by [6], but in our framework we admit a wider set of connectives inside the body of programs rules. In this paper, we make a first step in our pending task of embedding into FLOPER the weak unification algorithm of Bousi∼Prolog.…”

“…The new Similarity-based QLP(D) scheme, named SQLP(D), transforms a similarity relation into a set of QLP(D) rules able to emulate a unification by similarity process. In [27,6] they go a step further integrating constraints and proximity relations in their generic scheme, obtaining a really flexible programming framework named SQCLP.…”

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

“…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.…”