arXiv subjects

Manuel Carro

Publications and source records attributed to Manuel Carro.

At least 19 recordsLinked to original sources

arXiv2025

Proceedings 41st International Conference on Logic Programming

Since the first conference in Marseille in 1982, the International Conference on Logic Programming (ICLP) has been the premier international event for presenting research in logic programming. These proceedings include the Technical Communications of the 41st ICLP, held on 12-19 September 2025 at the University of Calabria in Rende, Italy. The papers and extended abstracts in this volume address the following areas and topics: theoretical foundations, language design and programming methodologies, program analysis and optimization, applications and implementation methodologies. This volume features contributions to three submission tracks of ICLP 2025: the Main track, IJCAI fast track, and Recently Published Research track.

cs.LO↗

arXiv2022

Building Information Modeling Using Constraint Logic Programming

Building Information Modeling (BIM) produces three-dimensional models of buildings combining the geometrical information with a wide range of properties. BIM is slowly but inevitably revolutionizing the architecture, engineering, and construction (AEC) industry. Buildings need to be compliant with regulations about stability, safety, and environmental impact. Manual compliance checking is tedious and error-prone, and amending flaws discovered only at construction time causes huge additional costs and delays. Several tools can check BIM models for conformance with rules/guidelines. For example, Singapore's CORENET e-Submission System checks fire safety. But since the current BIM exchange format only contains basic information of building objects, a separate, ad-hoc model pre-processing is required to determine, e.g., evacuation routes. Moreover, they face difficulties in adapting existing built-in rules and/or adding new ones (to cater for building regulations, that can vary not only among countries but also among parts of the same city), if at all possible. We propose the use of logic-based executable formalisms (CLP and Constraint ASP) to couple BIM models with advanced knowledge representation and reasoning capabilities. Previous experience shows that such formalisms can be used to uniformly capture and reason with knowledge (including ambiguity) in a large variety of domains. Additionally, incorporating checking within design tools makes it possible to ensure that models are rule-compliant at every step. This also prevents erroneous designs from having to be (partially) redone, which is also costly and burdensome. To validate our proposal, we implemented a preliminary reasoner under CLP(Q/R) and ASP with constraints and evaluated it with several BIM models. Under consideration for acceptance in Theory and Practice of Logic Programming (TPLP).

cs.LO↗

arXiv2021

Towards Dynamic Consistency Checking in Goal-directed Predicate Answer Set Programming

Goal-directed evaluation of Answer Set Programs is gaining traction thanks to its amenability to create AI systems that can, due to the evaluation mechanism used, generate explanations and justifications. s(CASP) is one of these systems and has been already used to write reasoning systems in several fields. It provides enhanced expressiveness w.r.t. other ASP systems due to its ability to use constraints, data structures, and unbound variables natively. However, the performance of existing s(CASP) implementations is not on par with other ASP systems: model consistency is checked once models have been generated, in keeping with the generate-and-test paradigm. In this work, we present a variation of the top-down evaluation strategy, termed Dynamic Consistency Checking, which interleaves model generation and consistency checking. This makes it possible to determine when a literal is not compatible with the denials associated to the global constraints in the program, prune the current execution branch, and choose a different alternative. This strategy is specially (but not exclusively) relevant in problems with a high combinatorial component. We have experimentally observed speedups of up to 90x w.r.t. the standard versions of s(CASP).

cs.AI↗

arXiv2021

Modeling and Reasoning in Event Calculus using Goal-Directed Constraint Answer Set Programming

Automated commonsense reasoning is essential for building human-like AI systems featuring, for example, explainable AI. Event Calculus (EC) is a family of formalisms that model commonsense reasoning with a sound, logical basis. Previous attempts to mechanize reasoning using EC faced difficulties in the treatment of the continuous change in dense domains (e.g., time and other physical quantities), constraints among variables, default negation, and the uniform application of different inference methods, among others. We propose the use of s(CASP), a query-driven, top-down execution model for Predicate Answer Set Programming with Constraints, to model and reason using EC. We show how EC scenarios can be naturally and directly encoded in s(CASP) and how it enables deductive and abductive reasoning tasks in domains featuring constraints involving both dense time and dense fluents.

cs.AI↗

arXiv2020

A Theoretical Study of (Full) Tabled Constraint Logic Programming

Logic programming with tabling and constraints (TCLP, tabled constraint logic programming) has been shown to be more expressive and, in some cases, more efficient than LP, CLP, or LP with tabling. In this paper we provide insights regarding the semantics, correctness, completeness, and termination of top-down execution strategies for full TCLP, i.e., TCLP featuring entailment checking in the calls and in the answers. We present a top-down semantics for TCLP and show that it is equivalent to a fixpoint semantics. We study how the constraints that a program generates can effectively impact termination, even for constraint classes that are not constraint compact, generalizing previous results. We also present how different variants of constraint projection impact the correctness and completeness of TCLP implementations. All of the presented characteristics are implemented (or can be experimented with) in Mod TCLP, a modular framework for Tabled Constraint Logic Programming, part of the Ciao Prolog logic programming system.

cs.LO↗

arXiv2020

Justifications for Goal-Directed Constraint Answer Set Programming

Ethical and legal concerns make it necessary for programs that may directly influence the life of people (via, e.g., legal or health counseling) to justify in human-understandable terms the advice given. Answer Set Programming has a rich semantics that makes it possible to very concisely express complex knowledge. However, justifying why an answer is a consequence from an ASP program may be non-trivial -- even more so when the user is an expert in a given domain, but not necessarily knowledgeable in ASP. Most ASP systems generate answers using SAT-solving procedures on ground rules that do not match how humans perceive reasoning. We propose using s(CASP), a query-driven, top-down execution model for predicate ASP with constraints to generate justification trees of (constrained) answer sets. The operational semantics of s(CASP) relies on backward chaining, which is intuitive to follow and lends itself to generating explanations that are easier to translate into natural language. We show how s(CASP) provides minimal justifications for, among others, relevant examples proposed in the literature, both as search trees but, more importantly, as explanations in natural language. We validate our design with real ASP applications and evaluate the cost of generating s(CASP) justification trees.

cs.LO↗

arXiv2019

Evaluation of the Implementation of an Abstract Interpretation Algorithm using Tabled CLP

CiaoPP is an analyzer and optimizer for logic programs, part of the Ciao Prolog system. It includes PLAI, a fixpoint algorithm for the abstract interpretation of logic programs which we adapt to use tabled constraint logic programming. In this adaptation, the tabling engine drives the fixpoint computation, while the constraint solver handles the LUB of the abstract substitutions of different clauses. That simplifies the code and improves performance, since termination, dependencies, and some crucial operations (e.g., branch switching and resumption) are directly handled by the tabling engine. Determining whether the fixpoint has been reached uses semantic equivalence, which can decide that two syntactically different abstract substitutions represent the same element in the abstract domain. Therefore, the tabling analyzer can reuse answers in more cases than an analyzer using syntactical equality. This helps achieve better performance, even taking into account the additional cost associated to these checks. Our implementation is based on the TCLP framework available in Ciao Prolog and is one-third the size of the initial fixpoint implementation in CiaoPP. Its performance has been evaluated by analyzing several programs using different abstract domains. This paper is under consideration for publication in Theory and Practice of Logic Programming (TPLP).

cs.PL↗

arXiv2018

Description, Implementation, and Evaluation of a Generic Design for Tabled CLP

Logic programming with tabling and constraints (TCLP, tabled constraint logic programming) has been shown to be more expressive and in some cases more efficient than LP, CLP or LP + tabling. Previous designs of TCLP systems did not fully use entailment to determine call / answer subsumption and did not provide a simple and well-documented interface to facilitate the integration of constraint solvers in existing tabling systems. We study the role of projection and entailment in the termination, soundness and completeness of TCLP systems, and present the design and an experimental evaluation of Mod TCLP, a framework that eases the integration of additional constraint solvers. Mod TCLP views constraint solvers as clients of the tabling system, which is generic w.r.t. the solver and only requires a clear interface from the latter. We validate our design by integrating four constraint solvers: a previously existing constraint solver for difference constraints, written in C; the standard versions of Holzbaur's CLP(Q) and CLP(R), written in Prolog; and a new constraint solver for equations over finite lattices. We evaluate the performance of our framework in several benchmarks using the aforementioned constraint solvers. Mod TCLP is developed in Ciao Prolog, a robust, mature, next-generation Prolog system. Under consideration in Theory and Practice of Logic Programming (TPLP).

cs.PL↗

arXiv2018

Constraint Answer Set Programming without Grounding

Extending ASP with constraints (CASP) enhances its expressiveness and performance. This extension is not straightforward as the grounding phase, present in most ASP systems, removes variables and the links among them, and also causes a combinatorial explosion in the size of the program. Several methods to overcome this issue have been devised: restricting the constraint domains (e.g., discrete instead of dense), or the type (or number) of models that can be returned. In this paper we propose to incorporate constraints into s(ASP), a goal-directed, top-down execution model which implements ASP while retaining logical variables both during execution and in the answer sets. The resulting model, s(CASP), can constrain variables that, as in CLP, are kept during the execution and in the answer sets. s(CASP) inherits and generalizes the execution model of s(ASP) and is parametric w.r.t. the constraint solver. We describe this novel execution model and show through several examples the enhanced expressiveness of s(CASP) w.r.t. ASP, CLP, and other CASP systems. We also report improved performance w.r.t. other very mature, highly optimized ASP systems in some benchmarks. This paper is under consideration for publication in Theory and Practice of Logic Programming (TPLP).

cs.PL↗

arXiv2017

Towards Automatic Learning of Heuristics for Mechanical Transformations of Procedural Code

The current trends in next-generation exascale systems go towards integrating a wide range of specialized (co-)processors into traditional supercomputers. Due to the efficiency of heterogeneous systems in terms of Watts and FLOPS per surface unit, opening the access of heterogeneous platforms to a wider range of users is an important problem to be tackled. However, heterogeneous platforms limit the portability of the applications and increase development complexity due to the programming skills required. Program transformation can help make programming heterogeneous systems easier by defining a step-wise transformation process that translates a given initial code into a semantically equivalent final code, but adapted to a specific platform. Program transformation systems require the definition of efficient transformation strategies to tackle the combinatorial problem that emerges due to the large set of transformations applicable at each step of the process. In this paper we propose a machine learning-based approach to learn heuristics to define program transformation strategies. Our approach proposes a novel combination of reinforcement learning and classification methods to efficiently tackle the problems inherent to this type of systems. Preliminary results demonstrate the suitability of this approach.

cs.PL↗

arXiv2017

Towards a Semantics-Aware Code Transformation Toolchain for Heterogeneous Systems

Obtaining good performance when programming heterogeneous computing platforms poses significant challenges. We present a program transformation environment, implemented in Haskell, where architecture-agnostic scientific C code with semantic annotations is transformed into functionally equivalent code better suited for a given platform. The transformation steps are represented as rules that can be fired when certain syntactic and semantic conditions are fulfilled. These rules are not hard-wired into the rewriting engine: they are written in a C-like language and are automatically processed and incorporated into the rewriting engine. That makes it possible for end-users to add their own rules or to provide sets of rules that are adapted to certain specific domains or purposes.

cs.PL↗

arXiv2016

Papers presented at the 32nd International Conference on Logic Programming (ICLP 2016)

This is the list of the full papers accepted for presentation at the 32nd International Conference on Logic Programming, New York City, USA, October 18-21, 2016. In addition to the main conference itself, ICLP hosted four pre-conference workshops, the Autumn School on Logic Programing, and a Doctoral Consortium. The final versions of the full papers will be published in a special issue of the journal Theory and Practice of Logic Programming (TPLP). We received eighty eight abstract submissions, of which twenty seven papers were accepted for publication as TPLP rapid communications. Papers deemed of sufficiently high quality to be presented as the conference, but not enough to be appear in TPLP, will be published as Technical Communications in the OASIcs series. Fifteen papers fell into this category.

cs.PL↗

arXiv2016

Towards a Semantics-Aware Transformation Toolchain for Heterogeneous Systems

Obtaining good performance when programming heterogeneous computing platforms poses significant challenges for the programmer. We present a program transformation environment, implemented in Haskell, where architecture-agnostic scientific C code with semantic annotations is transformed into functionally equivalent code better suited for a given platform. The transformation steps are formalized (and implemented) as rules which can be fired when certain syntactic and semantic conditions are met. These conditions are to be fulfilled by program properties which can be automatically inferred or, alternatively, stated as annotations in the source code. Rule selection can be guided by heuristics derived from a machine learning procedure which tries to capture how run-time characteristics (e.g., resource consumption or performance) are affected by the transformation steps.

cs.PL↗

arXiv2016

Towards Automatic Learning of Heuristics for Mechanical Transformations of Procedural Code

The current trend in next-generation exascale systems goes towards integrating a wide range of specialized (co-)processors into traditional supercomputers. However, the integration of different specialized devices increases the degree of heterogeneity and the complexity in programming such type of systems. Due to the efficiency of heterogeneous systems in terms of Watt and FLOPS per surface unit, opening the access of heterogeneous platforms to a wider range of users is an important problem to be tackled. In order to bridge the gap between heterogeneous systems and programmers, in this paper we propose a machine learning-based approach to learn heuristics for defining transformation strategies of a program transformation system. Our approach proposes a novel combination of reinforcement learning and classification methods to efficiently tackle the problems inherent to this type of systems. Preliminary results demonstrate the suitability of the approach for easing the programmability of heterogeneous systems.

cs.PL↗

arXiv2016

Proceedings of the First Workshop on Program Transformation for Programmability in Heterogeneous Architectures

This volume contains the proceedings of PROHA 2016, the first workshop on Program Transformation for Programmability in Heterogeneous Architectures, held on March 12, 2016 in Barcelona, Spain, as an affiliated workshop of CGO 2016, the 14th International Symposium on Code Generation and Optimization. Developing and maintaining high-performance applications and libraries for heterogeneous architectures while preserving its semantics and with a reasonable efficiency is a time-consuming task which is often only possible for experts. It often requires manually adapting sequential, platform-agnostic code to different infrastructures, and keeping the changes in all of these infrastructures in sync. These program modification tasks are costly and error-prone. Tools to assist in and, if possible, automate such transformations are of course of great interest. However, such tools may need significant reasoning and knowledge processing capabilities, including, for example, being able to process machine-understandable descriptions of the semantics of a piece of code is expected to do; to perform program transformations inside a context in which they are applicable; to use strategies to identify the sequence of transformations leading to the best resulting code; and others.

cs.PL↗

arXiv2014

Description and Optimization of Abstract Machines in a Dialect of Prolog

In order to achieve competitive performance, abstract machines for Prolog and related languages end up being large and intricate, and incorporate sophisticated optimizations, both at the design and at the implementation levels. At the same time, efficiency considerations make it necessary to use low-level languages in their implementation. This makes them laborious to code, optimize, and, especially, maintain and extend. Writing the abstract machine (and ancillary code) in a higher-level language can help tame this inherent complexity. We show how the semantics of most basic components of an efficient virtual machine for Prolog can be described using (a variant of) Prolog. These descriptions are then compiled to C and assembled to build a complete bytecode emulator. Thanks to the high level of the language used and its closeness to Prolog, the abstract machine description can be manipulated using standard Prolog compilation and optimization techniques with relative ease. We also show how, by applying program transformations selectively, we obtain abstract machine implementations whose performance can match and even exceed that of state-of-the-art, highly-tuned, hand-crafted emulators.

cs.PL↗

arXiv2012

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.

cs.PL↗

arXiv2011

Parallel Backtracking with Answer Memoing for Independent And-Parallelism

Goal-level Independent and-parallelism (IAP) is exploited by scheduling for simultaneous execution two or more goals which will not interfere with each other at run time. This can be done safely even if such goals can produce multiple answers. The most successful IAP implementations to date have used recomputation of answers and sequentially ordered backtracking. While in principle simplifying the implementation, recomputation can be very inefficient if the granularity of the parallel goals is large enough and they produce several answers, while sequentially ordered backtracking limits parallelism. And, despite the expected simplification, the implementation of the classic schemes has proved to involve complex engineering, with the consequent difficulty for system maintenance and extension, while still frequently running into the well-known trapped goal and garbage slot problems. This work presents an alternative parallel backtracking model for IAP and its implementation. The model features parallel out-of-order (i.e., non-chronological) backtracking and relies on answer memoization to reuse and combine answers. We show that this approach can bring significant performance advantages. Also, it can bring some simplification to the important engineering task involved in implementing the backtracking mechanism of previous approaches.

cs.PL↗