A flexible (C)LP-based approach to the analysis of object-oriented programs

Static analyses of object-oriented programs usually rely on intermediate representations that respect the original semantics while having a more uniform and basic syntax. Most of the work involving object-oriented languages and abstract interpretation usually omits the description of that language or just refers to the Control Flow Graph(CFG) it represents. However, this lack of formalization on one hand results in an absence of assurances regarding the correctness of the transformation and on the other it typically strongly couples the analysis to the source language. In this work we present a framework for analysis of object-oriented languages in which in a first phase we transform the input program into a representation based on Horn clauses. This allows on one hand proving the transformation correct attending to a simple condition and on the other being able to apply an existing analyzer for (constraint) logic programming to automatically derive a safe approximation of the semantics of the original program. The approach is flexible in the sense that the first phase decouples the analyzer from most languagedependent features, and correct because the set of Horn clauses returned by the transformation phase safely approximates the standard semantics of the input program. The resulting analysis is also reasonably scalable due to the use of mature, modular (C)LP-based analyzers. The overall approach allows us to report results for medium-sized programs.

A syntactic approach to combining functional notation, lazy evaluation, and higher-order in LP systems

Nondeterminism and partially instantiated data structures give logic programming expressive power beyond that of functional programming. However, functional programming often provides convenient syntactic features, such as having a designated implicit output argument, which allow function cali nesting and sometimes results in more compact code. Functional programming also sometimes allows a more direct encoding of lazy evaluation, with its ability to deal with infinite data structures. We present a syntactic functional extensión, used in the Ciao system, which can be implemented in ISO-standard Prolog systems and covers function application, predefined evaluable functors, functional definitions, quoting, and lazy evaluation. The extensión is also composable with higher-order features and can be combined with other extensions to ISO-Prolog such as constraints. We also highlight the features of the Ciao system which help implementation and present some data on the overhead of using lazy evaluation with respect to eager evaluation.

A documentation generator for (C)LP systems

We describe lpdoc, a tool which generates documentation manuals automatically from one or more logic program source files, written in Ciao, ISO-Prolog, and other (C)LP languages. It is particularly useful for documenting library modules, for which it automatically generates a rich description of the module interface. However, it can also be used quite successfully to document full applications. A fundamental advantage of using lpdoc is that it helps maintaining a true correspondence between the program and its documentation, and also identifying precisely to what versión of the program a given printed manual corresponds. The quality of the documentation generated can be greatly enhanced by including within the program text assertions (declarations with types, modes, etc. ...) for the predicates in the program, and machine-readable comments. One of the main novelties of lpdoc is that these assertions and comments are written using the Ciao system asseriion language, which is also the language of communication between the compiler and the user and between the components of the compiler. This allows a significant synergy among specification, debugging, documentation, optimization, etc. A simple compatibility library allows conventional (C)LP systems to ignore these assertions and comments and treat normally programs documented in this way. The documentation can be generated interactively from emacs or from the command line, in many formats including texinfo, dvi, ps, pdf, info, ascii, html/css, Unix nroff/man, Windows help, etc., and can include bibliographic citations and images, lpdoc can also genérate "man" pages (Unix man page format), nicely formatted plain ASCII "readme" files, installation scripts useful when the manuals are included in software distributions, brief descriptions in html/css or info formats suitable for inclusión in on-line Índices of manuals, and even complete WWW and info sites containing on-line catalogs of documents and software distributions. The lpdoc manual, all other Ciao system manuals, and parts of this paper are generated by lpdoc.

A system for automatically generating documentation for (C)LP programs

We describe lpdoc, a tool which generates documentation manuals automatically from one or more logic program source files, written in ISO-Prolog, Ciao, and other (C)LP languages. It is particularly useful for documenting library modules, for which it automatically generates a rich description of the module interface. However, it can also be used quite successfully to document full applications. A fundamental advantage of using lpdoc is that it helps maintaining a true correspondence between the program and its documentation, and also identifying precisely to what version of the program a given printed manual corresponds. The quality of the documentation generated can be greatly enhanced by including within the program text assertions (declarations with types, modes, etc.) for the predicates in the program, and machine-readable comments. One of the main novelties of lpdoc is that these assertions and comments are written using the Ciao system assertion language, which is also the language of communication between the compiler and the user and between the components of the compiler. This allows a significant synergy among specification, documentation, optimization, etc. A simple compatibility library allows conventional (C)LP systems to ignore these assertions and comments and treat normally programs documented in this way. The documentation can be generated in many formats including texinfo, dvi, ps, pdf, info, html/css, Unix nroff/man, Windows help, etc., and can include bibliographic citations and images. lpdoc can also generate “man” pages (Unix man page format), nicely formatted plain ascii “readme” files, installation scripts useful when the manuals are included in software distributions, brief descriptions in html/css or info formats suitable for inclusion in on-line indices of manuals, and even complete WWW and info sites containing on-line catalogs of documents and software distributions. The lpdoc manual, all other Ciao system manuals, and parts of this paper are generated by lpdoc.

The CIAO Multi-Dialect Compiler and System: An Experimentation Workbench for Future (C)LP Systems

CIAO is an advanced programming environment supporting Logic and Constraint programming. It offers a simple concurrent kernel on top of which declarative and non-declarative extensions are added via librarles. Librarles are available for supporting the ISOProlog standard, several constraint domains, functional and higher order programming, concurrent and distributed programming, internet programming, and others. The source language allows declaring properties of predicates via assertions, including types and modes. Such properties are checked at compile-time or at run-time. The compiler and system architecture are designed to natively support modular global analysis, with the two objectives of proving properties in assertions and performing program optimizations, including transparently exploiting parallelism in programs. The purpose of this paper is to report on recent progress made in the context of the CIAO system, with special emphasis on the capabilities of the compiler, the techniques used for supporting such capabilities, and the results in the áreas of program analysis and transformation already obtained with the system.

The CIAO multi-dialect compiler and system: An experimentation workbench for future (C)LP systems

Abstract is not available.

Lightweight compilation of (C)LP to JavaScript

We present and evaluate a compiler from Prolog (and extensions) to JavaScript which makes it possible to use (constraint) logic programming to develop the client side of web applications while being compliant with current industry standards. Targeting JavaScript makes (C)LP programs executable in virtually every modern computing device with no additional software requirements from the point of view of the user. In turn, the use of a very high-level language facilitates the development of high-quality, complex software. The compiler is a back end of the Ciao system and supports most of its features, including its module system and its rich language extension mechanism based on packages. We present an overview of the compilation process and a detailed description of the run-time system, including the support for modular compilation into separate JavaScript code. We demonstrate the maturity of the compiler by testing it with complex code such as a CLP(FD) library written in Prolog with attributed variables. Finally, we validate our proposal by measuring the performance of some LP and CLP(FD) benchmarks running on top of major JavaScript engines.

An automatic documentation generator for (C)LP - Reference manual

Lpdoc is an automatic program documentation generator for (C)LP systems. Lpdoc generates a reference manual automatically from one or more source files for a logic program (including ISO-Prolog, Ciao, many CLP systems, ...). It is particularly useful for documenting library modules, for which it automatically generates a description of the module interface. However, lpdoc can also be used quite successfully to document full applications and to generate nicely formatted plain ascii "readme" files. A fundamental advantage of using lpdoc to document programs is that it is much easier to maintain a true correspondence between the program and its documentation, and to identify precisely to what version of the program a given printed manual corresponds. The quality of the documentation generated can be greatly enhanced by including within the program text: • assertions (types, modes, etc. ...) for the predicates in the program, and • machine-readable comments (in the "literate programming" style). The assertions and comments included in the source file need to be written using the Ciao system assertion language. A simple compatibility library is available to make traditional (constraint) logic programming systems ignore these assertions and comments allowing normal treatment of programs documented in this way. The documentation is currently generated in HTML or texinf o format. From the texinf o output, printed and on-line manuals in several formats (dvi, ps, info, etc.) can be easily generated automatically, using publicly available tools, lpdoc can also generate 'man' pages (Unix man page format) as well as brief descriptions in html or emacs info formats suitable for inclusion in an on-line index of applications. In particular, lpdoc can create and maintain fully automatically WWW and info sites containing on-line versions of the documents it produces. The lpdoc manual (and the Ciao system manuals) are generated by lpdoc. Lpdoc is distributed under the GNU general public license. Note: lpdoc is fully supported on Linux, Mac OS X, and other Un*x-like systems. Due to the use of several Un*x-related utilities, some documentation back-ends may require Cygwin under Win32. This documentation corresponds to version 3.0 (2011/7/7, 16:33:15 CEST).

The AND-Prolog compiler system — Automatic parallelization tools for LP

This report presents an overview of the current work performed by us in the context of the efficient parallel implementation of traditional logic programming systems. The work is based on the &-Prolog System, a system for the automatic parallelization and execution of logic programming languages within the Independent And-parallelism model, and the global analysis and parallelization tools which have been developed for this system. In order to make the report self-contained, we first describe the "classical" tools of the &-Prolog system. We then explain in detail the work performed in improving and generalizing the global analysis and parallelization tools. Also, we describe the objectives which will drive our future work in this area.

Bernstein polynomials in element-free Galerkin method

In the recent decades, meshless methods (MMs), like the element-free Galerkin method (EFGM), have been widely studied and interesting results have been reached when solving partial differential equations. However, such solutions show a problem around boundary conditions, where the accuracy is not adequately achieved. This is caused by the use of moving least squares or residual kernel particle method methods to obtain the shape functions needed in MM, since such methods are good enough in the inner of the integration domains, but not so accurate in boundaries. This way, Bernstein curves, which are a partition of unity themselves,can solve this problem with the same accuracy in the inner area of the domain and at their boundaries.

Differential resultant formulas for linear od-polynomials.

Ponencia

Supporting pruning in tabled LP

This paper analyzes issues which appear when supporting pruning operators in tabled LP. A version of the once/1 control predicate tailored for tabled predicates is presented, and an implementation analyzed and evaluated. Using once/1 with answer-on-demand strategies makes it possible to avoid computing unneeded solutions for problems which can benefit from tabled LP but in which only a single solution is needed, such as model checking and planning. The proposed version of once/1 is also directly applicable to the efficient implementation of other optimizations, such as early completion, cut-fail loops (to, e.g., prune at the top level), if-then-else, and constraint-based branch-and-bound optimization. Although once/1 still presents open issues such as dependencies of tabled solutions on program history, our experimental evaluation confirms that it provides an arbitrarily large efficiency improvement in several application areas.

Differential resultants of super essential systems of linear OD-polynomials

The sparse differential resultant dres(P) of an overdetermined system P of generic nonhomogeneous ordinary differential polynomials, was formally defined recently by Li, Gao and Yuan (2011). In this note, a differential resultant formula dfres(P) is defined and proved to be nonzero for linear "super essential" systems. In the linear case, dres(P) is proved to be equal, up to a nonzero constant, to dfres(P*) for the supper essential subsystem P* of P.

Learning Bayesian networks from data by the incremental compilation of new network polynomials

Probabilistic graphical models are a huge research field in artificial intelligence nowadays. The scope of this work is the study of directed graphical models for the representation of discrete distributions. Two of the main research topics related to this area focus on performing inference over graphical models and on learning graphical models from data. Traditionally, the inference process and the learning process have been treated separately, but given that the learned models structure marks the inference complexity, this kind of strategies will sometimes produce very inefficient models. With the purpose of learning thinner models, in this master thesis we propose a new model for the representation of network polynomials, which we call polynomial trees. Polynomial trees are a complementary representation for Bayesian networks that allows an efficient evaluation of the inference complexity and provides a framework for exact inference. We also propose a set of methods for the incremental compilation of polynomial trees and an algorithm for learning polynomial trees from data using a greedy score+search method that includes the inference complexity as a penalization in the scoring function.

Conditional density approximations with mixtures of polynomials

Mixtures of polynomials (MoPs) are a non-parametric density estimation technique especially designed for hybrid Bayesian networks with continuous and discrete variables. Algorithms to learn one- and multi-dimensional (marginal) MoPs from data have recently been proposed. In this paper we introduce two methods for learning MoP approximations of conditional densities from data. Both approaches are based on learning MoP approximations of the joint density and the marginal density of the conditioning variables, but they differ as to how the MoP approximation of the quotient of the two densities is found. We illustrate and study the methods using data sampled from known parametric distributions, and we demonstrate their applicability by learning models based on real neuroscience data. Finally, we compare the performance of the proposed methods with an approach for learning mixtures of truncated basis functions (MoTBFs). The empirical results show that the proposed methods generally yield models that are comparable to or significantly better than those found using the MoTBF-based method.