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Local Search meta-heuristics have been proven a viable approach to solve difficult optimization problems. Their performance depends strongly on the search space landscape, as defined by a cost function and the selected neighborhood operators. In this paper we present a logic programming based framework, named Noodle, designed to generate bespoke Local Search neighborhoods tailored to specific discrete optimization problems. The proposed system consists of a domain specific language, which is inspired by logic programming, as well as a genetic programming solver, based on the grammar evolution algorithm. We complement the description with a preliminary experimental evaluation, where we synthesize efficient neighborhood operators for the traveling salesman problem, some of which reproduce well-known results., In Proceedings ICLP 2019, arXiv:1909.07646

International audience; We present a versatile architecture for AI-powered self-adaptive self-certifiable critical systems. It aims at supporting semi-automated low-cost re-certification for self-adaptive systems after each adaptation of their behavior to a persistent change in their operational environment throughout their lifecycle.

International audience; Many real-life applications can be formulated as Combinatorial Optimization Problems, the solution of which is often challenging due to their intrinsic difficulty. At present, the most effective methods to address the hardest problems entail the hybridization of metaheuristics and cooperative parallelism. Recently, a framework called CPLS has been proposed, which eases the cooperative parallelization of local search solvers. Being able to run different heuristics in parallel, CPLS has opened a new way to hybridize metaheuristics, thanks to its cooperative parallelism mechanism. However, CPLS is mainly designed for local search methods. In this paper we seek to overcome the current CPLS limitation, extending it to enable population-based metaheuristics in the hybridization process. We discuss an initial prototype implementation for Quadratic Assignment Problem combining a Genetic Algorithm with two local search procedures. Our experiments on hard instances of QAP show that this hybrid solver performs competitively w.r.t. dedicated QAP parallel solvers.

International audience; Local Search metaheuristics are a recognized means of solving hard combinatorialproblems. Over the last couple of decades, significant advances have beenmade in terms of the formalization, applicability and performance of these methods.Key to the performance aspect is the increased availability of parallel hardware,which turns out to be largely exploitable by this class of procedures. As thereal-life cases of combinatorial optimisation easily degrade into intractable territoryfor exact or approximation algorithms, local search metaheuristics hold undeniableinterest. This situation is further compounded by the good adequacy exhibited bythis class of search procedures for large-scale parallel operation. In this chapter weexplore and discuss ways which lead to parallelization in Local Search.

Declarative if–then rules have proven very useful in many applications of expert systems. They can be managed in deductive databases and evaluated using the well-known forward-chaining approach. For domain-experts, however, the syntax of rules becomes complicated quickly, and already many different knowledge representation formalisms exist. Expert knowledge is often acquired in story form using interviews. In this paper, we discuss its representation by defining domain-specific languages ( Dsl s) for declarative expert rules. They can be embedded in Prolog systems in internal Dsl s using term expansion and as external Dsl s using definite clause grammars and quasi-quotations – for more sophisticated syntaxes. Based on the declarative rules and the integration with the Prolog -based deductive database system DDbase , multiple rules acquired in practical case studies can be combined, compared, graphically analysed by domain-experts, and evaluated, resulting in an extensible system for expert knowledge. As a result, the actual modeling Dsl becomes executable; the declarative forward-chaining evaluation of deductive databases can be understood by the domain experts. Our Dsl for rules can be further improved by integrating ontologies and rule annotations.

We investigated the use of the Cell Broadband Engine (Cell/BE) for constraint-based local search and combinatorial optimization applications. We presented a parallel version of a constraint-based local search algorithm that was chosen because it fits very well the Cell/BE architecture because it requires neither shared memory nor communication among processors. The performance study on several large optimization benchmarks shows mostly linear time speedups, sometimes even super linear. These experiments were carried out on a dual-Cell IBM (Armonk, NY, USA) blade with 16 processors. Besides getting speedups, the execution times exhibit a much smaller variance that benefits applications where a timely reply is critical. Copyright © 2011 John Wiley & Sons, Ltd.

The boundary between the trusted inside and the untrusted outside blurs when a company adopts cloud computing. The organization's applications-and data-are no longer onsite, fundamentally changing the definition of a malicious insider.

International audience; We present the parallel implementation of a constraint-based Local Search algorithm and investigate its performance on several hardware plat-forms with several hundreds or thousands of cores. We chose as the basis for these experiments the Adaptive Search method, an efficient sequential Local Search method for Constraint Satisfaction Problems (CSP). After preliminary experiments on some CSPLib benchmarks, we detail the modeling and solving of a hard combinatorial problem related to radar and sonar applications: the Costas Array Problem. Performance evaluation on some classical CSP bench-marks shows that speedups are very good for a few tens of cores, and good up to a few hundreds of cores. However for a hard combinatorial search problem such as the Costas Array Problem, performance evaluation of the sequential version shows results outperforming previous Local Search implementations, while the parallel version shows nearly linear speedups up to 8,192 cores. The proposed parallel scheme is simple and based on independent multi-walks with no communication between processes during search. We also investigated a cooperative multi-walk scheme where processes share simple information, but this scheme does not seem to improve performance.

Constraint-Based Local Search (CBLS) consist in using Local Search methods [4] for solving Constraint Satisfaction Problems (CSP). In order to further improve the performance of Local Search, one possible option is to take advantage of the increasing availability of parallel computational resources. Parallel implementation of local search meta-heuristics has been studied since the early 90's, when multiprocessor machines started to become widely available, see [6]. One usually distinguishes between single-walk and multiple-walk methods: Single-walk methods consist in using parallelism inside a single search process, e.g. for parallelizing the exploration of the neighborhood, while multiple-walk methods (also called multi-start methods) consist in developing concurrent explorations of the search space, either independently (IW) or cooperatively (CW) with some communication between concurrent processes. Although good results can be achieved just with IW [1], a more sophisticated paradigm featuring cooperation between independent walks should bring better performance. We thus propose a general framework for cooperative search, which defines a flexible and parametric strategy based on the cooperative multi-walk (CW) scheme. The framework is oriented towards distributed architectures based on clusters of nodes, with the notion of "teams" running on nodes which group several individual search engines (e.g. multicore nodes). The idea is that teams are distributed and thus have limited inter-node communication. This framework allows the programmer to define aspects such as the degree of intensification and diversification present in the parallel search process. A good trade-off is essential to reach high performance. A preliminary implementation of the general CW framework has been done in the X10 programming language [5], and performance evaluation over a set of well-known benchmark CSPs shows that CW consistently outperforms IW.

Purpose—finding ways of improving learning in a formal higher education context. Design/methodology/approach—in the proposed model we will consider extending traditional content management systems, giving learners the possibility to add new materials and to rate them, and a hybrid strategy that combines technical recommendations with some profile-based filtering to offer adaptive and suitable sequencing learning content to learners. Findings—the experiment shows that our recommendation techniques are able to reflect the learners’ interests. Research limitations/implications—it’s necessary to demonstrate the contributions of these kinds of solutions to the learners’ success. Practical implications—theoretical and practical framework for future research in the field was developed. Originality/Value—the main contributions are the extended Learning Management System and the hybrid Recommender System, which implements a new proposal to evaluate learners’ similarities. Didėjantis technologinių inovacijų, eksperimentų bei tyrimų kiekis kuria scenarijus, kur laikas ir erdvė įgyja naujas reikšmes. Tinkamų mokymosi resursų identifikavimas bei išskyrimas iš gausybės potencialių variantų pasitelkiant internetą, tapo itin aktualia paslauga. Socialiniai tinklai ir debesų kompiuterijos sprendimai yra pastarųjų laikų inovacijos, užtikrinančios lengvesnę prieigą prie resursų (skirtingose laiko ir erdvės dimensijose, pasitelkiant daugybę terminalų). Nuotolinis mokymas įgauna vis didesnę reikšmę (netgi naudojamas kaip auditorinio mokymo pakaitalas). Žvelgti į mokymąsi per besimokančiojo prizmę, pateikiant jį kaip centrinę proceso ašį, yra viena iš naujųjų tendencijų mokymosi doktrinoje. Šis požiūris ypatingas tuo, jog besimokantysis yra laikomas žinių generatoriumi, o pačios žinios neatsiranda vien dėl trečiųjų šalių pateikiamos informacijos. Konstruktyvistinis mokymo modelis teigia, jog žinios atsiranda kaip socialinių konstruktų sąveikos rezultatas, kolaboratyvioje aplinkoje, dalyvaujant skirtingiems mokymosi subjektams. Ši aplinka reikalauja naujų technologijų, mokymosi metodų bei proceso paramos įrankių. Įvairūs besimokantieji su skirtingomis charakteristikomis, įgūdžiais, pajėgumu bei tikslais siekia tinkamiausių sau mokymosi veiklų bei resursų. Diferencijuotas turinys, adaptyvus mokymosi nuoseklumas yra keli iš reikalavimų siekiant įgyvendinti anksčiau minėtas mokymosi metodikas. Pagrindinis šios publikacijos tikslas – atrasti būdus, padedančius gerinti mokymo kokybę formaliojo aukštojo mokslo kontekste. Šiame straipsnyje pristatome į besimokantįjį orientuotą modelį, su personaliai pritaikytomis rekomendacijomis bei mokymosi veiklomis. Mes teigiame, jog besimokantieji gali skelbti naudingo turinio medžiagą bei ją vertinti. Šie turiniai gali būti susieti, siekiant padėti besimokantiesiems informacijos atrinkimo procese. Mūsų sistema numatys mokymosi aktyvumo rezultatus remdamasi besimokančiojo profiliu, hibridine rekomendacijų strategija bei kombinuojant sąlyginio ir kolaboratyvinio filtravimo metodus. Šie įverčiai padės besimokantiesiems pasirinkti tinkamiausias mokymosi veiklas. Mes teigiame, jog besimokančiųjų panašumai gali būti nustatomi remiantis įvertinimų rezultatais bei mokymosi istorija. Straipsnyje pristatomas motyvacinės mokymosi metodo tinkamumo prielaidos bei koncepcinės ir praktinės įžvalgos. Taip pat pristatome sistemos architektūros bei dizaino koncepcijas. Mūsų pasiūlytas modelis yra realizuotas pasitelkiant Moodle studijų valdymo sistemą. Realūs formaliųjų studijų eksperimentiniai kursai buvo vykdomi du mėnesius aukštojo mokslo institucijoje. Eksperimento rezultatai parodė, jog panašių sistemų vystymas padeda studijuojantiesiems diversifikuoti mokymosi kelius bei patirtis, padidina naudingą bendradarbiavimą, padeda geriau priimti sprendimus. Nepaisant to, šiems sprendimams įvaldyti reikia praktikos bei tinkamo dizaino realizavimo, siekiant palengvinti jų pritaikomumą.

The Costas Array Problem is a highly combina- torial problem linked to radar applications. We present in this paper its detailed modeling and solving by Adaptive Search, a constraint-based local search method. Experiments have been done on both sequential and parallel hardware up to several hundreds of cores. Performance evaluation of the sequential version shows results outperforming previous implementations, while the parallel version shows nearly linear speedups up to 8,192 cores.

We present a parallel implementation of a constraint-based local search algorithm and investigate its performance results on hard- ware with several hundreds of processors. We choose as basic constraint solving algorithm for these experiments the ”adaptive search” method, an efficient sequential local search method for Constraint Satisfaction Problems. The implemented algorithm is a parallel version of adaptive search in a multiple independent-walk manner, that is, each process is an independent search engine and there is no communication between the si- multaneous computations. Preliminary performance evaluation are very encouraging. On a variety of classical CSPs benchmarks from CSPLIB, speedups are very good for a few tens of cores, and good up to a few hundreds of processors. More challenging problems derived from real-life applications (Costas array) shows even better speedups, nearly optimal up to 256 cores.

The Costas Array Problem is a highly combinatorial problem linked to radar applications. We present in this paper its detailed modeling and solving by Adaptive Search, a constraint-based local search method. Experiments have been done on both sequential and parallel hardware up to several hundreds of cores. Performance evaluation of the sequential version shows results outperforming previous implementations, while the parallel version shows nearly linear speedups w.r.t. the sequential one, for instance 120 for 128 cores and 230 for 256 cores.

In this paper we propose to extend the logical framework ISCO (Information System COnstruction language) with an expressive means of representing and implicitly using temporal information. Moreover, we also provide a compilation scheme that targets a logic language with modularity and temporal reasoning.

International audience; We present a parallel implementation of a constraint-based local search algorithm and investigate its performance results on hardware with several hundreds of processors. We choose as basic constraint solving algorithm for these experiments the "adaptive search" method, an efficient sequential local search method for Constraint Satisfaction Problems. The implemented algorithm is a parallel version of adaptive search in a multiple independent-walk manner, that is, each process is an independent search engine and there is no communication between the simultaneous computations. Preliminary performance evaluation on a variety of classical CSPs benchmarks shows that speedups are very good for a few tens of processors, and good up to a few hundreds of processors.

We present a parallel implementation of a constraint-based local search algorithm and investigate its performance re- sults on hardware with several hundreds of processors.

International audience; We investigate the use of the Cell Broadband Engine (Cell/BE) for Combinatorial Optimization applications. We present a parallel version of a constraint-based Local Search algorithm which was chosen because it fits very well the Cell/BE architecture since it requires neither shared memory nor communication between processors. The performance study on several large optimization benchmarks shows mostly linear time speedups, even sometimes super-linear. These experiments were done on a Dual-Cell IBM Blade with 16 processors. Besides getting speedups, the execution times exhibit a much smaller variance, which benefits applications where a timely reply is critical.

International audience; We explore the use of the Cell Broadband Engine (Cell/BE for short) for combinatorial optimization applications: we present a parallel version of a constraint-based local search algorithm that has been implemented on a multiprocessor BladeCenter machine with twin Cell/BE processors (total of 16 SPUs per blade). This algorithm was chosen because it fits very well the Cell/BE architecture and requires neither shared memory nor communication between processors, while retaining a compact memory footprint. We study the performance on several large optimization benchmarks and show that this achieves mostly linear time speedups, even sometimes super-linear. This is possible because the parallel implementation might explore simultaneously different parts of the search space and therefore converge faster towards the best sub-space and thus towards a solution. Besides getting speedups, the resulting times exhibit a much smaller variance, which benefits applications where a timely reply is critical.

Albeit temporal reasoning and modularity are very prolific fields of research in Logic Programming (LP) we find few examples of their integration. Moreover, in those examples, time and modularity are considered orthogonal to each other. In this paper we propose the addition of temporal annotations to a modular extension of LP such that the usage of a module is influenced by temporal conditions. Besides illustrative examples we also provide an operational semantics together with a compiler, allowing this way for the development of applications based on such language.

Contextual Logic Programming [3] (CxLP) is a simple and powerful language that extends logic programming with mechanisms for modularization. The importance of temporal representation and reasoning is well known not only in the database community but also in the artificial intelligence one. In this paper we propose a language called Temporal Contextual Logic Programming. Besides giving a brief description of its operational semantics we also present a real-world application.

International audience; In this study, we started to investigate how the Partitioned Global Address Space (PGAS) programming language X10 would suit the implementation of a Constraint-Based Local Search solver. We wanted to code in this language because we expect to gain from its ease of use and independence from specifi c parallel architectures. We present the implementation strategy, and search for di fferent sources of parallelism. We discuss the algorithms, their implementations and present a performance evaluation on a representative set of benchmarks.

A logic programming based dialogue system with the capability of inferring user attitudes and accessing heterogeneous external relational databases while doing syntactic and semantic sentence parsing is presented.The system was built using a logic programming language - Prolog -, a development tool - ISCO [2] - and a language for representing actions - LUPS[4].An application of the developed system to the Universidade de Evora's Integrated Information System (SIIUE) was developed and some examples of typical dialogues are presented.

International audience; In this paper we address the problem of parallelizing local search. We propose a general framework where different local search en-gines cooperate (through communication) in the quest for a solution. Several parameters allow the user to instantiate and customize the frame-work, like the degree of intensification and diversification. We imple-mented a prototype in the X10 programming language based on the adaptive search method. We decided to use X10 in order to benefit from its ease of use and the architectural independence from parallel resources which it offers. Initial experiments prove the approach to be successful, as it outperforms previous systems as the number of processes increases.