2,998 research outputs found
On the incorporation of interval-valued fuzzy sets into the Bousi-Prolog system: declarative semantics, implementation and applications
In this paper we analyse the benefits of incorporating interval-valued fuzzy
sets into the Bousi-Prolog system. A syntax, declarative semantics and im-
plementation 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
A Transformation-based Implementation for CLP with Qualification and Proximity
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.Comment: 49 pages, 5 figures, 1 table, preliminary version of an article of
the same title, published as Technical Report SIC-4-10, Universidad
Complutense, Departamento de Sistemas Inform\'aticos y Computaci\'on, Madrid,
Spai
A Fuzzy Logic Programming Environment for Managing Similarity and Truth Degrees
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.Comment: In Proceedings PROLE 2014, arXiv:1501.0169
Aggregated fuzzy answer set programming
Fuzzy Answer Set programming (FASP) is an extension of answer set programming (ASP), based on fuzzy logic. It allows to encode continuous optimization problems in the same concise manner as ASP allows to model combinatorial problems. As a result of its inherent continuity, rules in FASP may be satisfied or violated to certain degrees. Rather than insisting that all rules are fully satisfied, we may only require that they are satisfied partially, to the best extent possible. However, most approaches that feature partial rule satisfaction limit themselves to attaching predefined weights to rules, which is not sufficiently flexible for most real-life applications. In this paper, we develop an alternative, based on aggregator functions that specify which (combination of) rules are most important to satisfy. We extend upon previous work by allowing aggregator expressions to define partially ordered preferences, and by the use of a fixpoint semantics
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