Gotowe bibliografie tematyczne / Constraint Satisfaction Programming

Gotowa bibliografia na temat „Constraint Satisfaction Programming”

Autor: Grafiati
Data publikacji: 4 czerwca 2021
Data aktualizacji: 20 lutego 2023

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Artykuły w czasopismach na temat "Constraint Satisfaction Programming"

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Van Hentenryck, Pascal, Helmut Simonis i Mehmet Dincbas. "Constraint satisfaction using constraint logic programming". Artificial Intelligence 58, nr 1-3 (grudzień 1992): 113–59. http://dx.doi.org/10.1016/0004-3702(92)90006-j.

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APT, KRZYSZTOF R., i ERIC MONFROY. "Constraint programming viewed as rule-based programming". Theory and Practice of Logic Programming 1, nr 6 (listopad 2001): 713–50. http://dx.doi.org/10.1017/s1471068401000072.

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We study here a natural situation when constraint programming can be entirely reduced to rule-based programming. To this end we explain first how one can compute on constraint satisfaction problems using rules represented by simple first-order formulas. Then we consider constraint satisfaction problems that are based on predefined, explicitly given constraints. To solve them we first derive rules from these explicitly given constraints and limit the computation process to a repeated application of these rules, combined with labeling. We consider two types of rule here. The first type, that we call equality rules, leads to a new notion of local consistency, called rule consistency that turns out to be weaker than arc consistency for constraints of arbitrary arity (called hyper-arc consistency in Marriott & Stuckey (1998)). For Boolean constraints rule consistency coincides with the closure under the well-known propagation rules for Boolean constraints. The second type of rules, that we call membership rules, yields a rule-based characterization of arc consistency. To show feasibility of this rule-based approach to constraint programming, we show how both types of rules can be automatically generated, as CHR rules of Frühwirth (1995). This yields an implementation of this approach to programming by means of constraint logic programming. We illustrate the usefulness of this approach to constraint programming by discussing various examples, including Boolean constraints, two typical examples of many valued logics, constraints dealing with Waltz's language for describing polyhedral scenes, and Allen's qualitative approach to temporal logic.
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Booth, Kyle E. C., Bryan O'Gorman, Jeffrey Marshall, Stuart Hadfield i Eleanor Rieffel. "Quantum-accelerated constraint programming". Quantum 5 (28.09.2021): 550. http://dx.doi.org/10.22331/q-2021-09-28-550.

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Constraint programming (CP) is a paradigm used to model and solve constraint satisfaction and combinatorial optimization problems. In CP, problems are modeled with constraints that describe acceptable solutions and solved with backtracking tree search augmented with logical inference. In this paper, we show how quantum algorithms can accelerate CP, at both the levels of inference and search. Leveraging existing quantum algorithms, we introduce a quantum-accelerated filtering algorithm for the alldifferent global constraint and discuss its applicability to a broader family of global constraints with similar structure. We propose frameworks for the integration of quantum filtering algorithms within both classical and quantum backtracking search schemes, including a novel hybrid classical-quantum backtracking search method. This work suggests that CP is a promising candidate application for early fault-tolerant quantum computers and beyond.
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Rossi, Francesca, Kristen Brent Venable i Toby Walsh. "Preferences in Constraint Satisfaction and Optimization". AI Magazine 29, nr 4 (28.12.2008): 58. http://dx.doi.org/10.1609/aimag.v29i4.2202.

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We review constraint-based approaches to handle preferences. We start by defining the main notions of constraint programming, then give various concepts of soft constraints and show how they can be used to model quantitative preferences. We then consider how soft constraints can be adapted to handle other forms of preferences, such as bipolar, qualitative, and temporal preferences. Finally, we describe how AI techniques such as abstraction, explanation generation, machine learning, and preference elicitation, can be useful in modelling and solving soft constraints.
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Rossi, Francesca. "Constraint satisfaction problems in logic programming". ACM SIGART Bulletin, nr 106 (październik 1988): 24–28. http://dx.doi.org/10.1145/54350.54352.

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LEUNG, HO-FUNG, i KEITH L. CLARK. "Constraint Satisfaction in Distributed Concurrent Logic Programming". Journal of Symbolic Computation 21, nr 4-6 (kwiecień 1996): 699–714. http://dx.doi.org/10.1006/jsco.1996.0037.

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Alpuente, María, Moreno Falaschi i Giorgio Levi. "Incremental constraint satisfaction for equational logic programming". Theoretical Computer Science 142, nr 1 (maj 1995): 27–57. http://dx.doi.org/10.1016/0304-3975(94)00224-7.

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Ho Geun Lee, Ronald M. Lee i Gang Yu. "Constraint logic programming for qualitative and quantitative constraint satisfaction problems". Decision Support Systems 16, nr 1 (styczeń 1996): 67–83. http://dx.doi.org/10.1016/0167-9236(94)00057-3.

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ZHANG, YUANLIN, i ROLAND H. C. YAP. "Solving functional constraints by variable substitution". Theory and Practice of Logic Programming 11, nr 2-3 (4.02.2011): 297–322. http://dx.doi.org/10.1017/s1471068410000591.

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AbstractFunctional constraints and bi-functional constraints are an important constraint class in Constraint Programming (CP) systems, in particular for Constraint Logic Programming (CLP) systems. CP systems with finite domain constraints usually employ Constraint Satisfaction Problem(s)-based solvers which use local consistency, for example, arc consistency. We introduce a new approach which is based instead on variable substitution. We obtain efficient algorithms for reducing systems involving functional and bi-functional constraints together with other nonfunctional constraints. It also solves globally any CSP where there exists a variable such that any other variable is reachable from it through a sequence of functional constraints. Our experiments on random problems show that variable elimination can significantly improve the efficiency of solving problems with functional constraints.
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Ligęza, Antoni. "Models and Tools for Improving Efficiency in Constraint Logic Programming". Decision Making in Manufacturing and Services 5, nr 1 (3.10.2011): 69–78. http://dx.doi.org/10.7494/dmms.2011.5.1.69.

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Constraint Satisfaction Problems typically exhibit strong combinatorial explosion. In this paper we present some models and techniques aimed at improving efficiency in Constraint Logic Programming. A hypergraph model of constraints is presented and an outline of strategy planning approach focused on entropy minimization is put forward. An example cryptoaritmetic problem is explored in order to explain the proposed approach.
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Rozprawy doktorskie na temat "Constraint Satisfaction Programming"

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Nightingale, Peter. "Consistency and the quantified constraint satisfaction problem /". St Andrews, 2007. http://hdl.handle.net/10023/759.

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Egri, László. "The complexity of constraint satisfaction problems and symmetric Datalog /". Thesis, McGill University, 2007. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=101843.

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Constraint satisfaction problems (CSPs) provide a unified framework for studying a wide variety of computational problems naturally arising in combinatorics, artificial intelligence and database theory. To any finite domain D and any constraint language Γ (a finite set of relations over D), we associate the constraint satisfaction problem CSP(Γ): an instance of CSP(Γ) consists of a list of variables x1, x2,..., x n and a list of constraints of the form "(x 7, x2,..., x5) ∈ R" for some relation R in Γ. The goal is to determine whether the variables can be assigned values in D such that all constraints are simultaneously satisfied. The computational complexity of CSP(Γ) is entirely determined by the structure of the constraint language Γ and, thus, one wishes to identify classes of Γ such that CSP(Γ) belongs to a particular complexity class.
In recent years, logical and algebraic perspectives have been particularly successful in classifying CSPs. A major weapon in the arsenal of the logical perspective is the database-theory-inspired logic programming language called Datalog. A Datalog program can be used to solve a restricted class of CSPs by either accepting or rejecting a (suitably encoded) set of input constraints. Inspired by Dalmau's work on linear Datalog and Reingold's breakthrough that undirected graph connectivity is in logarithmic space, we use a new restriction of Datalog called symmetric Datalog to identify a class of CSPs solvable in logarithmic space. We establish that expressibility in symmetric Datalog is equivalent to expressibility in a specific restriction of second order logic called Symmetric Restricted Krom Monotone SNP that has already received attention for its close relationship with logarithmic space.
We also give a combinatorial description of a large class of CSPs lying in L by showing that they are definable in symmetric Datalog. The main result of this thesis is that directed st-connectivity and a closely related CSP cannot be defined in symmetric Datalog. Because undirected st-connectivity can be defined in symmetric Datalog, this result also sheds new light on the computational differences between the undirected and directed st-connectivity problems.
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Thornton, John. "Constraint Weighting Local Search for Constraint Satisfaction". Thesis, Griffith University, 2000. http://hdl.handle.net/10072/367954.

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One of the challenges for the constraint satisfaction community has been to develop an automated approach to solving Constraint Satisfaction Problems (CSPs) rather than creating specific algorithms for specific problems. Much of this work has concentrated on the development and improvement of general purpose backtracking techniques. However, the success of relatively simple local search techniques on larger satisfiability problems [Selman et a!. 1992] and CSPs such as the n-queens [Minton et al. 1992] has caused interest in applying local search to constraint satisfaction. In this thesis we look at the usefulness of constraint weighting as a local search technique for constraint satisfaction. The work is based on the clause weighting ideas of Selman and Kautz [1993] and Moths [1993] and applies, evaluates and extends these ideas from the satisfiability domain to the more general domain of CSPs. Specifically, the contributions of the thesis are: 1. The introduction of a local search taxonomy. We examine the various better known local search techniques and recognise four basic strategies: restart, randomness, memory and weighting. 2. The extension of the CSP modelling framework. In order to represent and efficiently solve more realistic problems we extend the C SP modelling framework to include array-based domains and array-based domain use constraints. 3. The empirical evaluation of constraint weighting. We compare the performance of three constraint weighting strategies on a range of CSP and satisflability problems and with several other local search techniques. We find that no one technique dominates in all problem domains. 4. The characterisation of constraint weighting performance. Based on our empirical study we identiIS' the weighting behaviours and problem features that favour constrtt weighting. We conclude weighting does better on structured problems where the algorithm can recognise a harder sub-group of constraints. 5. The extension of constraint weighting. We introduce an efficient arc weighting algorithm that additionally weights connections between constraints that are simultaneously violated at a local minimum. This algorithm is empirically shown to outperform standard constraint weighting on a range of CSPs and within a general constraint solving system. Also we look at combining constraint weighting with other local search heuristics and find that these hybrid techniques can do well on problems where the parent algorithms are evenly matched. 6. The application of constraint weighting to over constrained domains. Our empirical work suggests constraint weighting does well for problems with distinctions between constraint groups. This led us to investigate solving real-world over constrained problems with hard and soft constraint groups and to introduce two dynamic constraint weighting heuristics that maintain a distinction between hard and soft constraint groups while still adding weights to violated constraints in a local minimum. In an empirical study, the dynamic schemes are shown to outperform other fixed weighting and non-weighting systems on a range of real world problems. In addition, the performance of weighting is shown to degrade less severely when soft constraints are added to the system, suggesting constraint weighting is especially applicable to realistic, hard and soft constraint problems
Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Computing and Information Technology
Science, Environment, Engineering and Technology
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Thornton, John Richard, i n/a. "Constraint Weighting Local Search for Constraint Satisfaction". Griffith University. School of Computing and Information Technology, 2000. http://www4.gu.edu.au:8080/adt-root/public/adt-QGU20050901.142439.

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One of the challenges for the constraint satisfaction community has been to develop an automated approach to solving Constraint Satisfaction Problems (CSPs) rather than creating specific algorithms for specific problems. Much of this work has concentrated on the development and improvement of general purpose backtracking techniques. However, the success of relatively simple local search techniques on larger satisfiability problems [Selman et a!. 1992] and CSPs such as the n-queens [Minton et al. 1992] has caused interest in applying local search to constraint satisfaction. In this thesis we look at the usefulness of constraint weighting as a local search technique for constraint satisfaction. The work is based on the clause weighting ideas of Selman and Kautz [1993] and Moths [1993] and applies, evaluates and extends these ideas from the satisfiability domain to the more general domain of CSPs. Specifically, the contributions of the thesis are: 1. The introduction of a local search taxonomy. We examine the various better known local search techniques and recognise four basic strategies: restart, randomness, memory and weighting. 2. The extension of the CSP modelling framework. In order to represent and efficiently solve more realistic problems we extend the C SP modelling framework to include array-based domains and array-based domain use constraints. 3. The empirical evaluation of constraint weighting. We compare the performance of three constraint weighting strategies on a range of CSP and satisflability problems and with several other local search techniques. We find that no one technique dominates in all problem domains. 4. The characterisation of constraint weighting performance. Based on our empirical study we identiIS' the weighting behaviours and problem features that favour constrtt weighting. We conclude weighting does better on structured problems where the algorithm can recognise a harder sub-group of constraints. 5. The extension of constraint weighting. We introduce an efficient arc weighting algorithm that additionally weights connections between constraints that are simultaneously violated at a local minimum. This algorithm is empirically shown to outperform standard constraint weighting on a range of CSPs and within a general constraint solving system. Also we look at combining constraint weighting with other local search heuristics and find that these hybrid techniques can do well on problems where the parent algorithms are evenly matched. 6. The application of constraint weighting to over constrained domains. Our empirical work suggests constraint weighting does well for problems with distinctions between constraint groups. This led us to investigate solving real-world over constrained problems with hard and soft constraint groups and to introduce two dynamic constraint weighting heuristics that maintain a distinction between hard and soft constraint groups while still adding weights to violated constraints in a local minimum. In an empirical study, the dynamic schemes are shown to outperform other fixed weighting and non-weighting systems on a range of real world problems. In addition, the performance of weighting is shown to degrade less severely when soft constraints are added to the system, suggesting constraint weighting is especially applicable to realistic, hard and soft constraint problems
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Padmanabhuni, Srinivas. "Logic programming with stable models for constraint satisfaction". Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape3/PQDD_0009/NQ60011.pdf.

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Zhang, Lixi. "Solving the timetabling problem using constraint satisfaction programming". Access electronically, 2005. http://www.library.uow.edu.au/adt-NWU/public/adt-NWU20051104.155838/index.html.

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Thorstensen, Evgenij. "Hybrid tractability of constraint satisfaction problems with global constraints". Thesis, University of Oxford, 2013. http://ora.ox.ac.uk/objects/uuid:05707b54-69e3-40eb-97e7-63b1a178c701.

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A wide range of problems can be modelled as constraint satisfaction problems (CSPs), that is, a set of constraints that must be satisfied simultaneously. Constraints can either be represented extensionally, by explicitly listing allowed combinations of values, or intensionally, whether by an equation, propositional logic formula, or other means. Intensionally represented constraints, known as global constraints, are a powerful modelling technique, and many modern CSP solvers provide them. We give examples to show how problems that deal with product configuration can be modelled with such constraints, and how this approach relates to other modelling formalisms. The complexity of CSPs with extensionally represented constraints is well understood, and there are several known techniques that can be used to identify tractable classes of such problems. For CSPs with global constraints, however, many of these techniques fail, and far fewer tractable classes are known. In order to remedy this state of affairs, we undertake a systematic review of research into the tractability of CSPs. In particular, we look at CSPs with extensionally represented constraints in order to understand why many of the techniques that give tractable classes for this case fail for CSPs with global constraints. The above investigation leads to two discoveries. First, many restrictions on how the constraints of a CSP interact implicitly rely on a property of extensionally represented constraints to guarantee tractability. We identify this property as being a bound on the number of solutions in key parts of the instance, and find classes of global constraints that also possess this property. For such classes, we show that many known tractability results apply. Furthermore, global constraints allow us to treat entire CSP instances as constraints. We combine this observation with the above result, and obtain new tractable classes of CSPs by dividing a CSP into smaller CSPs drawn from known tractable classes. Second, for CSPs that simply do not possess the above property, we look at how the constraints of an instance overlap, and how assignments to the overlapping parts extend to the rest of the problem. We show that assignments that extend in the same way can be identified. Combined with a new structural restriction, this observation leads to a second set of tractable classes. We conclude with a summary, as well as some observations about potential for future work in this area.
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Nightingale, Peter William. "Consistency and the Quantified Constraint Satisfaction Problem". Thesis, University of St Andrews, 2007. http://hdl.handle.net/10023/759.

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Constraint satisfaction is a very well studied and fundamental artificial intelligence technique. Various forms of knowledge can be represented with constraints, and reasoning techniques from disparate fields can be encapsulated within constraint reasoning algorithms. However, problems involving uncertainty, or which have an adversarial nature (for example, games), are difficult to express and solve in the classical constraint satisfaction problem. This thesis is concerned with an extension to the classical problem: the Quantified Constraint Satisfaction Problem (QCSP). QCSP has recently attracted interest. In QCSP, quantifiers are allowed, facilitating the expression of uncertainty. I examine whether QCSP is a useful formalism. This divides into two questions: whether QCSP can be solved efficiently; and whether realistic problems can be represented in QCSP. In attempting to answer these questions, the main contributions of this thesis are the following: - the definition of two new notions of consistency; - four new constraint propagation algorithms (with eight variants in total), along with empirical evaluations; - two novel schemes to implement the pure value rule, which is able to simplify QCSP instances; - a new optimization algorithm for QCSP; - the integration of these algorithms and techniques into a solver named Queso; - and the modelling of the Connect 4 game, and of faulty job shop scheduling, in QCSP. These are set in context by a thorough review of the QCSP literature.
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Nilsson, Josefin. "Generating Playback Sequences of Songs with Constraint Satisfaction Programming". Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-279298.

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With the right songs at the right time, a playback sequence of songs can fulfil the two fundamentals of a good music listening experience: the desire for repetition and the desire for surprise. Many businesses play continuous music in their locations with automatic playback generation, and for them, a good music experience can improve the overall customer satisfaction as well as increase sales. This is something that Soundtrack Your Brand, a Stockholm based mu- sic streaming company, seize by offering their clients music tailored for their business. One way of generating playback sequences of songs given a playlist and at the same time define criteria that the sequence should fulfil is by modelling it as a Constraint Satisfaction Problem. The problem can be defined as given a playlist of songs as input, generate a playback sequence of the songs such as a set of constraints over the attributes of the songs in the playback is satisfied. This thesis investigates if Constraint Satisfaction Programming is a viable approach for generating playback sequences and if so, how efficiently it can be done. A Constraint Satisfaction Programming sequencer is implemented which models three types of constraints and uses a Hill Climbing approach to solve the playback problem. The proposed sequencer is benchmarked against the current solution at Soundtrack Your Brand, which is a greedy algorithm, as well as a purely random shuffle. The results show that the proposed sequencer outperforms both the current solution and a random shuffle with regards to how well they satisfied the stated constraints. The efficiency of the sequencer is measured with regards to run-time and memory usage in different scenarios where the results indicate a viable performance when the size of the playlist increases. When comparing the proposed sequencer to the current solution, it is found that the sequencer has a short run-time for each of the playlist size that was tested. By combining the benchmarking- and efficiency evaluation results, the thesis concludes that it is possible to generate playback sequences with Constraint Satisfaction Programming and that it can be done efficiently enough for it to be run in a production environment.
Med rätt låt vid rätt tidpunkt kan uppspelningssekvenser av låtar uppfylla de två grundläggande egenskaperna av en bra musiklyssningsupplevelse: önskan för upprepning och önskan för överraskningar. Många företag spelar musik kontinuerligt i sina lokaler med automatisk uppspelningsordning, och för dem kan en bra musikupplevelse förbättra den generella kundbelåtenheten samt öka försäljningen. Det här är något som Soundtrack Your Brand, ett musik- streamingföretag baserat i Stockholm, utnyttjar genom att erbjuda sina kunder musik skräddarsydd för deras företag. Ett sätt att generera uppspelningssekvenser av låtar givet en spellista och samtidigt definiera kriterier sekvensen ska uppfylla är genom att modellera det som ett villkorsproblem. Problemet kan definieras som att givet en spellista av låtar som indata, generera en uppspelningssekvens av låtar så att en mängd villkor över låtarna i sekvensens attribut är uppfyllda. Det här examensarbetet undersöker om Constraint Satisfaction Programming är en möjlig lösning för att generera uppspelningssekvenser och i så fall, hur effektivt det går att göra det. En Constraint Satisfaction Programming-sekvenserare implementeras som modellerar tre olika typer av villkor och an- vänder ett Hill Climbing-tillvägagångssätt för att lösa uppspelningsproblemet. Den föreslagna sekvenseraren jämförs med den nuvarande lösningen på Soundtrack Your Brand, som är en girig algoritm, samt en matematiskt slump- mässig blandning. Resultaten visar att den föreslagna lösningen presterar bättre än både den nuvarande lösningen och den slumpmässiga blandningen när det kommer till hur väl de uppfyller de definierade villkoren. Sekvenserarens effektivitets mäts med avseende på körtid om minnesanvändning i olika scenarion där resultaten indikerar bra prestanda när storleken på spellistan ökar. När den föreslagna sekvenseraren jämförs med den nuvarande lösningen har sekvenseraren kortare körtid för alla storlekar av spellistor som testades. Genom att kombinera resultaten av jämförelsen och effektivitetsutvärderingen slår det här examensarbetet fast att det är möjligt att generera uppspelningssekvenser med Constraint Satisfaction Programming och att det kan göras tillräckligt effektivt för att köras i en produktionsmiljö.
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Gharbi, Nebras. "On compressing and parallelizing constraint satisfaction problems". Thesis, Artois, 2015. http://www.theses.fr/2015ARTO0406/document.

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La programmation par contraintes est un cadre puissant utilisé pour modéliser et résoudre des problèmes combinatoires, employant des techniques d'intelligence artificielle, de la recherche opérationnelle, de théorie des graphes,..., etc. L'idée de base de la programmation par contraintes est que l'utilisateur exprime ses contraintes et qu'un solveur de contraintes cherche une ou plusieurs solutions.Les problèmes de satisfaction de contraintes (CSP), sont au cœur de la programmation par contraintes. Ce sont des problèmes de décision où nous recherchons des états ou des objets satisfaisant un certain nombre de contraintes ou de critères. Ces problèmes de décision revoient vrai, si le problème admet une solution, faux, sinon. Les problèmes de satisfaction de contraintes sont le sujet de recherche intense tant en recherche opérationnelle qu'en intelligence artificielle. Beaucoup de CSPs exigent la combinaison d'heuristiques et de méthode d'inférences combinatoires pour les résoudre dans un temps raisonnable.Avec l'amélioration des ordinateurs, la résolution de plus grands problèmes devient plus facile. Bien qu'il y ait plus de capacités offertes par la nouvelle génération de machines, les problèmes industriels deviennent de plus en plus grand ce qui implique un espace _norme pour les stocker et aussi plus de temps pour les résoudre.Cette thèse s'articule autour des techniques d'optimisation de la résolution des CSPs en raisonnant sur plusieurs axes.Dans la première partie, nous traitons la compression des contraintes table. Nous proposons deux méthodes différentes pour la compression des contraintes de table. Les deux approches sont basées sur la recherche des motifs fréquents pour éviter la redondance. Cependant, la façon de définir un motif, la détection des motifs fréquents et la nouvelle représentation compacte diffère significativement. Nous présentons pour chacune des approches un algorithme de filtrage.La seconde partie est consacrée à une autre façon d'optimiser la résolution de CSP qui est l'utilisation d'une architecture parallèle. Nous proposons une méthode où nous utilisons une architecture parallèle pour améliorer le processus de résolution en établissant des cohérences parallèles. En fait, les esclaves envoient à leur maître le résultat obtenu après avoir établi la cohérence partielle en tant que nouveaux faits. Le maître, à son tour essaye de profiter d'eux en enlevant les valeurs correspondantes
Constraint Programming (CP) is a powerful paradigm used for modelling and solving combinatorial constraint problems that relies on a wide range of techniques coming from artificial intelligence, operational research, graph theory,..., etc. The basic idea of constraint programming is that the user expresses its constraints and a constraint solver seeks a solution. Constraint Satisfaction Problems (CSP), is a framework at the heart of CP problems. They correspond to decision problems where we seek for states or objects satisfying a number of constraints or criteria. These decision problems have two answers to the question they encode: true, if the problem admits a solution, false, otherwise. CSPs are the subject of intense research in both artificial intelligence and operations research. Many CSPs require the combination of heuristics and combinatorial optimization methods to solve them in a reasonable time.With the improvement of computers, larger and larger problems can be solved. However, the size of industrial problems grow faster which requires a vast amount of memory space to store them and entail great difficulties to solve them. In this thesis, our contributions can be divided into two main parts. In the first part, we deal with the most used kind of constraints, which are table constraints. We proposed two compressed forms of table constraints. Both of them are based on frequent patterns search in order to avoid redundancy. However, the manner of defining pattern, the patterns-detecting process and the new compact representation differ significantly. For each form, we propose a filtering algorithm. In the second part, we explore another way to optimize CSP solving which is the use of a parallel architecture. In fact, we enhance the solving process by establishing parallel consistencies. Different workers send to their master the result of establishing partial consistencies as new discovered facts. The master, in its turns tries to benefit from them by removing corresponding values
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Książki na temat "Constraint Satisfaction Programming"

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Hentenryck, Pascal Van. Constraint satisfaction in logic programming. Cambridge, Mass: MIT Press, 1989.

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Hjerpe, Torkel. High-level specification and efficient solving of constraint satisfaction problems. Uppsala: Computing Science Dept., Uppsala University, 1995.

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1963-, Bliek Christian, Jermann Christophe 1975- i Neumaier A, red. Global optimization and constraint satisfaction: First international workshop global constraint optimization and constraint satisfaction, COCOS 2002, Valbonne-Sophia Antipolis, France, October 2-4, 2002 : revised selected papers. Berlin: Springer-Verlag, 2003.

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1975-, Jermann Christophe, Neumaier A i Sam Djamila, red. Global optimization and constraint satisfaction: Second international workshop, COCOS 2003, Lausanne, Switzerland, November 18-21, 2003 : revised selected papers. Berlin: Springer, 2005.

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Wahbi, Mohamed. Algorithms and ordering heuristics for distributed constraint satisfaction problems. London, UK: ISTE, 2013.

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Miguel, Ian. Dynamic flexible constraint satisfaction and its application to AI planning. London: Springer, 2004.

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Global Optimization and Constraint Satisfaction: First International Workshop Global Constraint Optimization and Constraint Satisfaction, COCOS 2002, Valbonne-Sophia ... Papers (Lecture Notes in Computer Science). Springer, 2004.

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Wahbi, Mohamed. Algorithms and Ordering Heuristics for Distributed Constraint Satisfaction Problems. Wiley & Sons, Incorporated, John, 2013.

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Wahbi, Mohamed. Algorithms and Ordering Heuristics for Distributed Constraint Satisfaction Problems. Wiley & Sons, Incorporated, John, 2013.

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Wahbi, Mohamed. Algorithms and Ordering Heuristics for Distributed Constraint Satisfaction Problems. Wiley & Sons, Incorporated, John, 2013.

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Części książek na temat "Constraint Satisfaction Programming"

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Freuder, Eugene C. "Exploiting Structure in Constraint Satisfaction Problems". W Constraint Programming, 51–74. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-642-85983-0_3.

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Mayoh, Brian, Enn Tyugu i Tarmo Uustalu. "Constraint Satisfaction and Constraint Programming: A Brief Lead-In". W Constraint Programming, 1–16. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-642-85983-0_1.

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Solnon, Christine, i Narendra Jussien. "Constraint Satisfaction Problems". W Ant Colony Optimization and Constraint Programming, 31–52. Hoboken, NJ USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118557563.ch3.

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Kirousis, Lefteris M. "Fast parallel constraint satisfaction". W Automata, Languages and Programming, 418–29. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/3-540-56939-1_91.

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Jung, Victor, i Jean-Charles Régin. "Checking Constraint Satisfaction". W Integration of Constraint Programming, Artificial Intelligence, and Operations Research, 332–47. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-78230-6_21.

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Hooker, J. N. "Convex Programming Methods for Global Optimization". W Global Optimization and Constraint Satisfaction, 46–60. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11425076_4.

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Dvořák, Zdeněk, Daniel Král’ i Ondřej Pangrác. "Locally Consistent Constraint Satisfaction Problems". W Automata, Languages and Programming, 469–80. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-27836-8_41.

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Ringwelski, Georg, i Youssef Hamadi. "Boosting Distributed Constraint Satisfaction". W Principles and Practice of Constraint Programming - CP 2005, 549–62. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11564751_41.

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Cruz, Jorge, i Pedro Barahona. "Constraint Satisfaction Differential Problems". W Principles and Practice of Constraint Programming – CP 2003, 259–73. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45193-8_18.

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Mitchell, David G. "Resolution and Constraint Satisfaction". W Principles and Practice of Constraint Programming – CP 2003, 555–69. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45193-8_38.

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Streszczenia konferencji na temat "Constraint Satisfaction Programming"

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Berrani, Sid-Ahmed, Haykel Boukadida i Patrick Gros. "Constraint Satisfaction Programming for Video Summarization". W 2013 IEEE International Symposium on Multimedia (ISM). IEEE, 2013. http://dx.doi.org/10.1109/ism.2013.38.

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Dev Gupta, Sharmi, Begum Genc i Barry O'Sullivan. "Explanation in Constraint Satisfaction: A Survey". W Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/601.

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Much of the focus on explanation in the field of artificial intelligence has focused on machine learning methods and, in particular, concepts produced by advanced methods such as neural networks and deep learning. However, there has been a long history of explanation generation in the general field of constraint satisfaction, one of the AI's most ubiquitous subfields. In this paper we survey the major seminal papers on the explanation and constraints, as well as some more recent works. The survey sets out to unify many disparate lines of work in areas such as model-based diagnosis, constraint programming, Boolean satisfiability, truth maintenance systems, quantified logics, and related areas.
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Canbaz, Baris, Bernard Yannou i Pierre-Alain Yvars. "Constraint Programming Simulation of a Distributed Set-Based Design Framework With Control Indicators". W ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/detc2012-70857.

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In the New Product Development processes, there are usually multiple players interacting through coupled design activities with conflicting design objectives. We use Set-based design approach and Constraint Satisfaction Problem solving techniques to deal with the multiplayer, multi-objective design problem. In order to observe the states of the players during the design process, we develop satisfaction and progress indicators and combine them into a wellbeing indicator. In this paper we simulate different player behaviors on a two-player, multi-objective engineering design problem of a hollow cylindrical cantilever beam. During the simulation cases, at each simulation iteration automatic players are prioritized regarding their wellbeing states for proposing constraints in order to find a set of consistent solutions that improve their satisfaction states. Therefore during the simulation process, while solution space converges to a single solution, epistemic uncertainty is reduced and players’ satisfaction states are improved in wellbeing equilibrium.
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Latour, Anna Louise D., Behrouz Babaki i Siegfried Nijssen. "Stochastic Constraint Propagation for Mining Probabilistic Networks". W Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/159.

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A number of data mining problems on probabilistic networks can be modeled as Stochastic Constraint Optimization and Satisfaction Problems, i.e., problems that involve objectives or constraints with a stochastic component. Earlier methods for solving these problems used Ordered Binary Decision Diagrams (OBDDs) to represent constraints on probability distributions, which were decomposed into sets of smaller constraints and solved by Constraint Programming (CP) or Mixed Integer Programming (MIP) solvers. For the specific case of monotonic distributions, we propose an alternative method: a new propagator for a global OBDD-based constraint. We show that this propagator is (sub-)linear in the size of the OBDD, and maintains domain consistency. We experimentally evaluate the effectiveness of this global constraint in comparison to existing decomposition-based approaches, and show how this propagator can be used in combination with another data mining specific constraint present in CP systems. As test cases we use problems from the data mining literature.
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Scemama, G. "Signal plan design for complex intersections: a constraint satisfaction programming approach". W Eighth International Conference on Road Traffic Monitoring and Control. IEE, 1996. http://dx.doi.org/10.1049/cp:19960296.

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Ortiz-Bayliss, Jose Carlos, Ender Ozcan, Andrew J. Parkes i Hugo Terashima-Marin. "A genetic programming hyper-heuristic: Turning features into heuristics for constraint satisfaction". W 2013 13th UK Workshop on Computational Intelligence (UKCI). IEEE, 2013. http://dx.doi.org/10.1109/ukci.2013.6651304.

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Yannou, Bernard, Faysal Moreno, Henri J. Thevenot i Timothy W. Simpson. "Faster Generation of Feasible Design Points". W ASME 2005 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2005. http://dx.doi.org/10.1115/detc2005-85449.

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Design space exploration during conceptual design is an active research field. Most approaches generate a number of feasible design points (complying with the constraints) and apply graphical post-processing to visualize correlations between variables, the Pareto frontier or a preference structure among the design solutions. The generation of feasible design points is often a statistical (Monte Carlo) generation of potential candidates sampled within initial variable domains, followed by a verification of constraint satisfaction, which may become inefficient if the design problem is highly constrained since a majority of candidates that are generated do not belong to the (small) feasible solution space. In this paper, we propose to perform a preliminary analysis with Constraint Programming techniques that are based on interval arithmetic to dramatically prune the solution space before using statistical (Monte Carlo) methods to generate candidates in the design space. This method requires that the constraints are expressed in an analytical form. A case study involving truss design under uncertainty is presented to demonstrate that the computation time for generating a given number of feasible design points is greatly improved using the proposed method. The integration of both techniques provides a flexible mechanism to take successive design refinements into account within a dynamic process of design under uncertainty.
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Talbot, Pierre. "Search Strategies as Synchronous Processes (Extended Abstract)". W Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/766.

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Solving constraint satisfaction problems (CSP) efficiently depends on the solver configuration and the search strategy. However, it is difficult to customize the constraint solvers because they are not modular enough, and it is hard to create new search strategies by composition. To solve these problems, we propose spacetime programming, a paradigm based on lattices and synchronous process calculi that views search strategies as processes working collaboratively towards the resolution of a CSP. We implement the compiler of the language and use it to replace the search module of Choco, a state of the art constraint solver, with an efficient spacetime program that offers better modularity and compositionality of search strategies.
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Eriksson, Leif, i Victor Lagerkvist. "Improved Algorithms for Allen's Interval Algebra: a Dynamic Programming Approach". W Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/258.

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The constraint satisfaction problem (CSP) is an important framework in artificial intelligence used to model e.g. qualitative reasoning problems such as Allen's interval algebra A. There is strong practical incitement to solve CSPs as efficiently as possible, and the classical complexity of temporal CSPs, including A, is well understood. However, the situation is more dire with respect to running time bounds of the form O(f(n)) (where n is the number of variables) where existing results gives a best theoretical upper bound 2^O(n * log n) which leaves a significant gap to the best (conditional) lower bound 2^o(n). In this paper we narrow this gap by presenting two novel algorithms for temporal CSPs based on dynamic programming. The first algorithm solves temporal CSPs limited to constraints of arity three in O(3^n) time, and we use this algorithm to solve A in O((1.5922n)^n) time. The second algorithm tackles A directly and solves it in O((1.0615n)^n), implying a remarkable improvement over existing methods since no previously published algorithm belongs to O((cn)^n) for any c. We also extend the latter algorithm to higher dimensions box algebras where we obtain the first explicit upper bound.
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Koriche, Frédéric, Sylvain Lagrue, Éric Piette i Sébastien Tabary. "Constraint-Based Symmetry Detection in General Game Playing". W Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/40.

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Symmetry detection is a promising approach for reducing the search tree of games. In General Game Playing (GGP), where any game is compactly represented by a set of rules in the Game Description Language (GDL), the state-of-the-art methods for symmetry detection rely on a rule graph associated with the GDL description of the game. Though such rule-based symmetry detection methods can be applied to various tree search algorithms, they cover only a limited number of symmetries which are apparent in the GDL description. In this paper, we develop an alternative approach to symmetry detection in stochastic games that exploits constraint programming techniques. The minimax optimization problem in a GDL game is cast as a stochastic constraint satisfaction problem (SCSP), which can be viewed as a sequence of one-stage SCSPs. Minimax symmetries are inferred according to themicrostructure complement of these one-stage constraint networks. Based on a theoretical analysis of this approach, we experimentally show on various games that the recent stochastic constraint solver MAC-UCB, coupled with constraint-based symmetry detection, significantly outperforms the standard Monte Carlo Tree Search algorithms, coupled with rule-based symmetry detection. This constraint-driven approach is also validated by the excellent results obtained by our player during the last GGP competition.
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