28 research outputs found
Probabilistic Constraint Logic Programming
This paper addresses two central problems for probabilistic processing
models: parameter estimation from incomplete data and efficient retrieval of
most probable analyses. These questions have been answered satisfactorily only
for probabilistic regular and context-free models. We address these problems
for a more expressive probabilistic constraint logic programming model. We
present a log-linear probability model for probabilistic constraint logic
programming. On top of this model we define an algorithm to estimate the
parameters and to select the properties of log-linear models from incomplete
data. This algorithm is an extension of the improved iterative scaling
algorithm of Della-Pietra, Della-Pietra, and Lafferty (1995). Our algorithm
applies to log-linear models in general and is accompanied with suitable
approximation methods when applied to large data spaces. Furthermore, we
present an approach for searching for most probable analyses of the
probabilistic constraint logic programming model. This method can be applied to
the ambiguity resolution problem in natural language processing applications.Comment: 35 pages, uses sfbart.cl
Inference with Constrained Hidden Markov Models in PRISM
A Hidden Markov Model (HMM) is a common statistical model which is widely
used for analysis of biological sequence data and other sequential phenomena.
In the present paper we show how HMMs can be extended with side-constraints and
present constraint solving techniques for efficient inference. Defining HMMs
with side-constraints in Constraint Logic Programming have advantages in terms
of more compact expression and pruning opportunities during inference.
We present a PRISM-based framework for extending HMMs with side-constraints
and show how well-known constraints such as cardinality and all different are
integrated. We experimentally validate our approach on the biologically
motivated problem of global pairwise alignment
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
ΠΠΎΠ΄Π΅Π»ΠΈ Π°Π±Π΄ΡΠΊΡΠΈΠ²Π½ΠΎΠΉ Π»ΠΎΠ³ΠΈΠΊΠΈ ΠΏΡΠΈ Π°Π½Π°Π»ΠΈΠ·Π΅ Π½Π°Π΄Π΅ΠΆΠ½ΠΎΡΡΠΈ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠ½ΠΎΠ³ΠΎ ΠΎΠ±Π΅ΡΠΏΠ΅ΡΠ΅Π½ΠΈΡ
Proposed approach allows estimation of software reliability in case of absence of reliability information of its components.ΠΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½Π½ΡΠΉ ΠΏΠΎΠ΄Ρ
ΠΎΠ΄ Π΄Π°Π΅Ρ Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡΡ ΠΎΡΠ΅Π½ΠΊΠΈ Π½Π°Π΄Π΅ΠΆΠ½ΠΎΡΡΠΈ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠ½ΡΡ
ΡΠΈΡΡΠ΅ΠΌ ΠΏΡΠΈ Π½Π΅Π΄ΠΎΡΡΠ°ΡΠΎΡΠ½ΠΎΠΉ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ ΠΎ Π½Π°Π΄Π΅ΠΆΠ½ΠΎΡΡΠΈ ΠΌΠΎΠ΄ΡΠ»Π΅ΠΉ ΠΈ ΠΊΠΎΠΌΠΏΠΎΠ½Π΅Π½Ρ, ΠΈΠ· ΠΊΠΎΡΠΎΡΡΡ
ΡΠΎΡΡΠΎΠΈΡ ΡΠΈΡΡΠ΅ΠΌΠ°
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