23,068 research outputs found
Fuzzy order-sorted feature logic
Order-Sorted Feature (OSF) logic is a knowledge representation and reasoning
language based on function-denoting feature symbols and set-denoting sort
symbols ordered in a subsumption lattice. OSF logic allows the construction of
record-like terms that represent classes of entities and that are themselves
ordered in a subsumption relation. The unification algorithm for such
structures provides an efficient calculus of type subsumption, which has been
applied in computational linguistics and implemented in constraint logic
programming languages such as LOGIN and LIFE and automated reasoners such as
CEDAR. This work generalizes OSF logic to a fuzzy setting. We give a flexible
definition of a fuzzy subsumption relation which generalizes Zadeh's inclusion
between fuzzy sets. Based on this definition we define a fuzzy semantics of OSF
logic where sort symbols and OSF terms denote fuzzy sets. We extend the
subsumption relation to OSF terms and prove that it constitutes a fuzzy partial
order with the property that two OSF terms are subsumed by one another in the
crisp sense if and only if their subsumption degree is greater than 0. We show
how to find the greatest lower bound of two OSF terms by unifying them and how
to compute the subsumption degree between two OSF terms, and we provide the
complexity of these operations.Comment: Accepted for publication in Fuzzy Sets and System
A finite-valued solver for disjunctive fuzzy answer set programs
Fuzzy Answer Set Programming (FASP) is a declarative programming paradigm which extends the flexibility and expressiveness of classical Answer Set Programming (ASP), with the aim of modeling continuous application domains. In contrast to the availability of efficient ASP solvers, there have been few attempts at implementing FASP solvers. In this paper, we propose an implementation of FASP based on a reduction to classical ASP. We also develop a prototype implementation of this method. To the best of our knowledge, this is the first solver for disjunctive FASP programs. Moreover, we experimentally show that our solver performs well in comparison to an existing solver (under reasonable assumptions) for the more restrictive class of normal FASP programs
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
A Declarative Semantics for CLP with Qualification and Proximity
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 paper we propose a
new scheme SQCLP 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.Comment: 17 pages, 26th Int'l. Conference on Logic Programming (ICLP'10
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
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