Catálogo de publicaciones - libros

During the past few years mobile phones have become an ubiquitous companion. Starting as a handset to cellular networks today’s mobile phones are devices capable of intelligent services itself. The IASON project aims at providing mobile users with location-aware personalized information. Motivated by the development of powerful mobile devices and the semantic web, we define a . In such an environment, so-called service nodes are installed at chosen points of interest. These service nodes broadcast semantically annotated messages to nearby mobile users using bluetooth ad-hoc wireless technology. Location-awareness is given implicitly by being in the wireless range of a service node and comes free of costs just as the communication itself. A describing the users interests and disinterests is managed on her mobile device. This user profile is used to sort out unwanted messages by performing matchmaking between the semantic annotation of the messages and the user profile (see [8]). The protection of privacy requires the profile to stay on the phone and to perform all necessary reasoning on the phone.

The relationship between abstract interpretation [2] and partial evaluation [5] has received considerable attention and (partial) integrations have been proposed starting from both the partial deduction (see e.g. [6] and its references) and abstract interpretation perspectives. Abstract interpretation-based analyzers (such as the analyzer [9,4]) generally compute a [1] in order to propagate (abstract) call and success information by performing fixpoint computations when needed. On the other hand, partial deduction methods [7] incorporate powerful techniques for on-line specialization including (concrete) call propagation and unfolding.

(PTTP) [5] is a well known extension of Prolog for answering queries in first-order logic. PTTP is based on the idea that [1]. As explained in [5], PTTP is an efficient realisation of the Model Elimination (ME) calculus [2] that utilises five extensions of standard Prolog:

1. It uses a algorithm with the occur-check;

2. It uses a strategy based on iterative-deepening;

3. It adds of the clauses in the theory in order to provide entry points for all of the literals in those clauses;

4. It uses when unfolding the query in order to overcome the incompleteness of Prolog’s SLD resolution;

5. It adds contrapositives for the negation of the query in order to extract from successful computations.

Although it is equipped for full clausal reasoning, PTTP has been tailored to some notable Horn clause applications by removing those features deemed unnecessary in the Horn case. For example, the Inductive Logic Programming (ILP) system Progol5 [3] includes a simplified PTTP technique that uses contrapositives in order to query the negative literals entailed by a given Horn theory, but does not support ancestor resolution or indefinite answers.

Constraint Logic Programming has been successful as a programming language, and more recently, as a model of executable specifications. There have been numerous works which use CLP to model programs and which use an adaptation of the CLP proof system for proving certain properties, for example, the XMC system [3] uses SLG resolution on alternation-free -calculus formulas, and the work on deductive model checking [1] model for CTL properties on transition systems represented as CLP rules. These, amongst other works, cover a limited class of programs and use specialized proof methods. In our work, we present a systematic method to model a general class of programs, and provide adaptations of the CLP proof systems in order to provide a systematic and general proof method.

We consider the problem of strong equivalence [1] under the infinite-valued semantics [2] (which is a purely model-theoretic version of the well-founded semantics). We demonstrate that two programs are now strongly equivalent if and only if they are logically equivalent under the infinite-valued logic of [2]. In particular, we show that for propositional programs strong equivalence is decidable but coNP-complete. Our results have a direct practical implication for the well-founded semantics since, as we demonstrate, if two programs are strongly equivalent under the infinite-valued semantics, then they are also strongly equivalent under the well-founded semantics.

During the past years, our research group has been working in the design and implementation of Logic Programming Systems. In previous work, we have produced systems to support sequential, parallel and distributed execution of Prolog; to support novel techniques and models, such as tabling, through the YapTab system [1], or the Extended Andorra Model (EAM), through the BEAM [2]; and to support the combination of the above, such as parallel tabling [3]. With the IMPACT project we want to combine the power of tabling with that of EAM in order to produce an execution model with advanced control strategies that guarantees termination, avoids looping, reduces the search space, and is less sensitive to goal ordering. Ultimately, we believe such a system will allow novel logic programming applications.

In this paper, we expose the use of CLP in a Textual Data Mining Task. Text Mining process is here applied to a corpus of semi-structured documents like seminary and job announcement. Such documents contain semi-structured sections each of which will be recognised by an automaton whose language is characterised by a set of CLP rules.

Constraint Satisfaction Problems (CSP) provide a modelling framework for many computer aided decision making problems. Many of these problems are associated to an optimization criterion. Solving a CSP consists in finding an assignment of values to the variables that satisfies the constraints and optimizes a given objective function (in case of an optimization problem). In this paper, we extend our framework for genetic algorithms (GA) as suggested by the reviewers of our previous ICLP paper [5]. Our purpose is not to solve efficiently the Balanced Academic Curriculum Problem (BACP) [2] but to combine a genetic algorithm with constraint programming techniques and to propose a general modelling framework to precisely design such hybrid resolution process and highlight their characteristics and properties.

The MYDDAS project () is developing a deductive database system by coupling Yap Prolog with MySQL[1]. Although this coupling approach between a logic system and a relational database management system has been quite explored[5], our system tries to go a step further in terms of the degree of in the interface architecture between the two systems. Examples of this improved integration include the representation of relational tuples directly in choice-points, with a transparent support for over EDB predicates; the extended use of the tabling engine of Yap[3], with the ability to persistently store the table data structure in MySQL relations; and the development of automatic view-level transformations using information from existing MySQL indexes and MySQL query optimizer. The MYDDAS system should also be able to explore the combination of tabling with or-parallelism provided by the OPTYap engine[4] of Yap in the concurrent evaluation of database goals.

This work is part of the SILK project which aims at supporting using logic programming [2]. The main idea of our approach is to collect and manage meta-information on the sources to be integrated. These pieces of information are stored in the model warehouse of the SILK system in the form of models, constraints and mappings. By using logic, all these are represented in a uniform way.