Gotowe bibliografie tematyczne / Constraint satisfaction

Gotowa bibliografia na temat „Constraint satisfaction”

Autor: Grafiati
Data publikacji: 4 czerwca 2021
Data aktualizacji: 29 lipca 2024

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Artykuły w czasopismach na temat "Constraint satisfaction"

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Pu, Jiantao, i Karthik Ramani. "Priority-Based Geometric Constraint Satisfaction". Journal of Computing and Information Science in Engineering 7, nr 4 (14.06.2007): 322–29. http://dx.doi.org/10.1115/1.2795301.

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A well-constrained geometric system seldom occurs in practice, especially at the sketch-based initial conceptual design stage. Usually, it is either under- or overconstrained because design is a progressive process and it is difficult for a designer to specify all involved constraints in a consistent way. This paper presents a priority-based graph-reduction solution, in which each constraint is assigned with a priority to guide the reduction of a geometric constraint graph. The advantage of this method lies in its ability to find the optimal solutions to a geometric constraint system automatically, without requiring interactive intervention from users.
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Detassis, Fabrizio, Michele Lombardi i Michela Milano. "Teaching the Old Dog New Tricks: Supervised Learning with Constraints". Proceedings of the AAAI Conference on Artificial Intelligence 35, nr 5 (18.05.2021): 3742–49. http://dx.doi.org/10.1609/aaai.v35i5.16491.

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Adding constraint support in Machine Learning has the potential to address outstanding issues in data-driven AI systems, such as safety and fairness. Existing approaches typically apply constrained optimization techniques to ML training, enforce constraint satisfaction by adjusting the model design, or use constraints to correct the output. Here, we investigate a different, complementary, strategy based on "teaching" constraint satisfaction to a supervised ML method via the direct use of a state-of-the-art constraint solver: this enables taking advantage of decades of research on constrained optimization with limited effort. In practice, we use a decomposition scheme alternating master steps (in charge of enforcing the constraints) and learner steps (where any supervised ML model and training algorithm can be employed). The process leads to approximate constraint satisfaction in general, and convergence properties are difficult to establish; despite this fact, we found empirically that even a naive setup of our approach performs well on ML tasks with fairness constraints, and on classical datasets with synthetic constraints.
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Frank, Jeremy. "Revisiting dynamic constraint satisfaction for model-based planning". Knowledge Engineering Review 31, nr 5 (listopad 2016): 429–39. http://dx.doi.org/10.1017/s0269888916000242.

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AbstractAs planning problems become more complex, it is increasingly useful to integrate complex constraints on time and resources into planning models, and use constraint reasoning approaches to help solve the resulting problems. Dynamic constraint satisfaction is a key enabler of automated planning in the presence of such constraints. In this paper, we identify some limitations with the previously developed theories of dynamic constraint satisfaction. We identify a minimum set of elementary transformations from which all other transformations can be constructed. We propose a new classification of dynamic constraint satisfaction transformations based on a formal criteria, namely the change in the fraction of solutions. This criteria can be used to evaluate elementary transformations of a constraint satisfaction problem as well as sequences of transformations. We extend the notion of transformations to include constrained optimization problems. We discuss how this new framework can inform the evolution of planning models, automated planning algorithms, and mixed-initiative planning.
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LIU, BING. "SPECIFIC CONSTRAINT HANDLING IN CONSTRAINT SATISFACTION PROBLEMS". International Journal on Artificial Intelligence Tools 03, nr 01 (marzec 1994): 79–96. http://dx.doi.org/10.1142/s0218213094000066.

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Abundant literatures exist on consistency techniques for solving Constraint Satisfaction Problems (CSPs). These literatures, however, focused mainly on finding efficient general techniques to achieve network consistency and to solve CSPs. So far, many techniques have been reported, e.g., node consistency, arc consistency, path consistency, k-consistency, forward checking, lookahead, partial lookahead, etc. Not enough attention has been given to individual constraints, and how constraint specific features may be exploited for more efficient consistency check. Many types of constraints exist in real problems, and each has its own features. These features may allow specific consistency techniques to be designed such that they are more efficient than the general algorithms. To analyze this issue, we divide a consistency algorithm into three parts: (1) activating constraints for check; (2) selecting the next constraint to be checked; and (3) checking the selected constraint. We will discuss how constraint specific features may influence each of these aspects and how special handling techniques may be designed to improve the efficiency. In order to allow these individual constraint handling techniques to be used, a new consistency algorithm is also proposed.
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Bulatov, Andrei A., i Dániel Marx. "Constraint satisfaction problems and global cardinality constraints". Communications of the ACM 53, nr 9 (wrzesień 2010): 99–106. http://dx.doi.org/10.1145/1810891.1810914.

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Deville, Yves, Olivier Barette i Pascal Van Hentenryck. "Constraint satisfaction over connected row-convex constraints". Artificial Intelligence 109, nr 1-2 (czerwiec 1999): 243–71. http://dx.doi.org/10.1016/s0004-3702(99)00012-0.

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Brito, Ismel, Amnon Meisels, Pedro Meseguer i Roie Zivan. "Distributed constraint satisfaction with partially known constraints". Constraints 14, nr 2 (15.05.2008): 199–234. http://dx.doi.org/10.1007/s10601-008-9048-x.

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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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Baykan, Can A., i Mark S. Fox. "Spatial synthesis by disjunctive constraint satisfaction". Artificial Intelligence for Engineering Design, Analysis and Manufacturing 11, nr 4 (wrzesień 1997): 245–62. http://dx.doi.org/10.1017/s0890060400003206.

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AbstractThe spatial synthesis problem addressed in this paper is the configuration of rectangles in 2D space, where the sides of the rectangles are parallel to an orthogonal coordinate system. Variables are the locations of the edges of the rectangles and their orientations. Algebraic constraints on these variables define a layout and constitute a constraint satisfaction problem. We give a new O(n2) algorithm for incremental path-consistency, which is applied after adding each algebraic constraint. Problem requirements are formulated as spatial relations between the rectangles, for example, adjacency, minimum distance, and nonoverlap. Spatial relations are expressed by Boolean combinations of the algebraic constraints; called disjunctive constraints. Solutions are generated by backtracking search, which selects a disjunctive constraint and instantiates its disjuncts. The selected disjuncts describe an equivalence class of configurations that is a significantly different solution. This method generates the set of significantly different solutions that satisfy all the requirements. The order of instantiating disjunctive constraints is critical for search efficiency. It is determined dynamically at each search state, using functions of heuristic measures called textures. Textures implement fail-first and prune-early strategies. Extensions to the model, that is, 3D configurations, configurations of nonrectangular shapes, constraint relaxation, optimization, and adding new rectangles during search are discussed.
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Duffy, Ken R., Charles Bordenave i Douglas J. Leith. "Decentralized Constraint Satisfaction". IEEE/ACM Transactions on Networking 21, nr 4 (sierpień 2013): 1298–308. http://dx.doi.org/10.1109/tnet.2012.2222923.

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Rozprawy doktorskie na temat "Constraint satisfaction"

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Pang, Wanlin. "Constraint structure in constraint satisfaction problems". Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape10/PQDD_0012/NQ39165.pdf.

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Bodirsky, Manuel. "Constraint satisfaction with infinite domains". Doctoral thesis, [S.l. : s.n.], 2004. http://deposit.ddb.de/cgi-bin/dokserv?idn=973605413.

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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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Engebretsen, Lars. "Approximate constraint satisfaction". Doctoral thesis, KTH, Numerical Analysis and Computer Science, NADA, 2000. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-2950.

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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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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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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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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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Fowler, David W. "Branching constraint satisfaction problems : sequential constrained decision making under uncertainty". Thesis, University of Aberdeen, 2002. http://digitool.abdn.ac.uk/R?func=search-advanced-go&find_code1=WSN&request1=AAIU153443.

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One of the main characteristics of our world is uncertainty. Making plans for the future is difficult, as we do not know exactly what the future holds. Companies must be flexible, ready to cope with the unpredictable demands that are placed on them. As a result, plans are often either short term, or tend to change soon after they are made. Another feature of the modern world is its pace. Decisions must be made quickly, or events may make them out of date before they can be implemented. In this thesis, we look at decision making problems in the presence of uncertainty about how the problem may develop over time, and in particular where the decisions must be made efficiently. Constraint based reasoning has proven to be a very successful technique for supporting decision making, but to date it has assumed static problems. In this thesis, we will show that constraint based methods can be used to reason about uncertain futures, and we will present a method which incorporates some ideas from decision theory to represent and solve such problems. In particular, we will formulate a class of problems, develop systematic optimisation search techniques, incomplete heuristic methods and compare with existing techniques.
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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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Książki na temat "Constraint satisfaction"

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Ghédira, Khaled, i Bernard Dubuisson, red. Constraint Satisfaction Problems. Hoboken, NJ USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118574522.

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Yokoo, Makoto. Distributed Constraint Satisfaction. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-59546-2.

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Tsang, Edward. Foundations of constraint satisfaction. Colchester: The author, 1996.

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Jermann, Christophe, Arnold Neumaier i Djamila Sam, red. Global Optimization and Constraint Satisfaction. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/b136292.

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Bliek, Christian, Christophe Jermann i Arnold Neumaier, red. Global Optimization and Constraint Satisfaction. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/b94062.

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

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Petke, Justyna. Bridging Constraint Satisfaction and Boolean Satisfiability. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-21810-6.

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Hyafil, Nathanael. Probabilistic planning with constraint satisfaction techniques. Ottawa: National Library of Canada, 2003.

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Güsgen, Hans Werner. CONSAT: A system for constraint satisfaction. London: Pitman, 1989.

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Güsgen, Hans Werner. CONSAT: A system for constraint satisfaction. London: Pitman, 1989.

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Części książek na temat "Constraint satisfaction"

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Yokoo, Makoto. "Constraint Satisfaction Problem". W Distributed Constraint Satisfaction, 1–45. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-59546-2_1.

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Yokoo, Makoto. "Distributed Constraint Satisfaction Problem". W Distributed Constraint Satisfaction, 47–54. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-59546-2_2.

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Yokoo, Makoto. "Asynchronous Backtracking". W Distributed Constraint Satisfaction, 55–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-59546-2_3.

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Yokoo, Makoto. "Asynchronous Weak-Commitment Search". W Distributed Constraint Satisfaction, 69–79. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-59546-2_4.

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Yokoo, Makoto. "Distributed Breakout". W Distributed Constraint Satisfaction, 81–92. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-59546-2_5.

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Yokoo, Makoto. "Distributed Consistency Algorithm". W Distributed Constraint Satisfaction, 93–100. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-59546-2_6.

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Yokoo, Makoto. "Handling Multiple Local Variables". W Distributed Constraint Satisfaction, 101–11. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-59546-2_7.

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Yokoo, Makoto. "Handling Over-Constrained Situations". W Distributed Constraint Satisfaction, 113–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-59546-2_8.

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Yokoo, Makoto. "Summary and Future Issues". W Distributed Constraint Satisfaction, 133–35. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-59546-2_9.

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Ghédira, Khaled, i Bernard Dubuisson. "Foundations of CSP". W Constraint Satisfaction Problems, 1–28. Hoboken, NJ USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118574522.ch1.

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Streszczenia konferencji na temat "Constraint satisfaction"

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Marsden, Gary C., F. Kiamilev, S. Esener i Sing H. Lee. "Optical Matrix Encoding for Constraint Satisfaction". W Optical Computing. Washington, D.C.: Optica Publishing Group, 1989. http://dx.doi.org/10.1364/optcomp.1989.mc2.

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Many Artificial Intelligence problems, such as theorem proving, computer vision, and expert systems, can be seen as constraint satisfaction problems1,2,3. In such problems, constraints are given on any possible solution. Most often, the problem is either explicitly stated in terms of allowed partial solutions or can be converted to such a representation. The objective is to find one or more solutions satisfying all constraints simultaneously, or the determination that no such solution exists. A simplistic approach to solving constraint satisfaction problems is to generate all possible solutions, then test each against the constraints to see if indeed they are satisfied. The process of backtracking provides a marginal improvement by testing increasingly larger partial solutions. Consistent Labelling is a more efficient procedure which eliminates allowed partial solutions that conflict with one another 2. A problem is represented in a constraint network. Arc and path Consistent Labelling eliminate allowed partial solutions that are inconsistent over the smallest closed loops. The remaining allowed partial solutions can then be used in an efficient backtracking search. Using Consistent Labelling on larger loops, it is possible to obtain solutions to constraint satisfaction problems without backtracking4.
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Gao, Shiqing, Jiaxin Ding, Luoyi Fu, Xinbing Wang i Chenghu Zhou. "Exterior Penalty Policy Optimization with Penalty Metric Network under Constraints". W Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. California: International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/443.

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In Constrained Reinforcement Learning (CRL), agents explore the environment to learn the optimal policy while satisfying constraints. The penalty function method has recently been studied as an effective approach for handling constraints, which imposes constraints penalties on the objective to transform the constrained problem into an unconstrained one. However, it is challenging to choose appropriate penalties that balance policy performance and constraint satisfaction efficiently. In this paper, we propose a theoretically guaranteed penalty function method, Exterior Penalty Policy Optimization (EPO), with adaptive penalties generated by a Penalty Metric Network (PMN). PMN responds appropriately to varying degrees of constraint violations, enabling efficient constraint satisfaction and safe exploration. We theoretically prove that EPO consistently improves constraint satisfaction with a convergence guarantee. We propose a new surrogate function and provide worst-case constraint violation and approximation error. In practice, we propose an effective smooth penalty function, which can be easily implemented with a first-order optimizer. Extensive experiments are conducted, showing that EPO outperforms the baselines in terms of policy performance and constraint satisfaction with a stable training process, particularly on complex tasks.
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Hosobe, Hiroshi. "Hierarchical nonlinear constraint satisfaction". W the 2004 ACM symposium. New York, New York, USA: ACM Press, 2004. http://dx.doi.org/10.1145/967900.967907.

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Rouahi, Aouatef, Kais Ben Salah i Khaled Ghedira. "Belief Constraint Satisfaction Problems". W 2015 IEEE/ACS 12th International Conference of Computer Systems and Applications (AICCSA). IEEE, 2015. http://dx.doi.org/10.1109/aiccsa.2015.7507108.

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Löffler, Sven, Ke Liu i Petra Hofstedt. "Decomposing Constraint Satisfaction Problems by Means of Meta Constraint Satisfaction Optimization Problems". W 11th International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and Technology Publications, 2019. http://dx.doi.org/10.5220/0007455907550761.

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Codognet, Philippe. "Quantum Annealing for Constraint Satisfaction and Constrained Optimization". W 15th International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and Technology Publications, 2023. http://dx.doi.org/10.5220/0011925200003393.

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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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Sample, Timothy, i Malak Mouhoub. "Augmenting spreadsheets with constraint satisfaction". W 2011 24th IEEE Canadian Conference on Electrical and Computer Engineering (CCECE). IEEE, 2011. http://dx.doi.org/10.1109/ccece.2011.6030616.

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Klin, Bartek, Eryk Kopczynski, Joanna Ochremiak i Szymon Torunczyk. "Locally Finite Constraint Satisfaction Problems". W 2015 30th Annual ACM/IEEE Symposium on Logic in Computer Science (LICS). IEEE, 2015. http://dx.doi.org/10.1109/lics.2015.51.

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Prager, John, Jennifer Chu-Carroll i Krzysztof Czuba. "Question answering using constraint satisfaction". W the 42nd Annual Meeting. Morristown, NJ, USA: Association for Computational Linguistics, 2004. http://dx.doi.org/10.3115/1218955.1219028.

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Raporty organizacyjne na temat "Constraint satisfaction"

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Cheng, Cheng-Chung, i Stephen F. Smith. Applying Constraint Satisfaction Techniques to Job Shop Scheduling. Fort Belvoir, VA: Defense Technical Information Center, styczeń 1995. http://dx.doi.org/10.21236/ada293583.

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Sadeh, Norman, Katia Sycara i Yalin Xiong. Backtracking Techniques for the Job Shop Scheduling Constraint Satisfaction Problem. Fort Belvoir, VA: Defense Technical Information Center, styczeń 1994. http://dx.doi.org/10.21236/ada289435.

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Blower, David J. Using Constraint Satisfaction Networks to Study Aircrew Selection for Advanced Cockpits. Fort Belvoir, VA: Defense Technical Information Center, styczeń 1992. http://dx.doi.org/10.21236/ada258151.

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Britton, Bruce K., i F. J. Eisenhart. Expertise, Text Coherence, and Constraint Satisfaction: Effects on Harmony and Settling Rate. Fort Belvoir, VA: Defense Technical Information Center, marzec 1993. http://dx.doi.org/10.21236/ada262703.

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Sadeh, Norman M., i Mark S. Fox. Variable and Value Ordering Heuristics for the Job Shop Scheduling Constraint Satisfaction Problem. Fort Belvoir, VA: Defense Technical Information Center, listopad 1995. http://dx.doi.org/10.21236/ada311303.

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Chaudhari, Gunavant. Simulation and emulation of massively parallel processor for solving constraint satisfaction problems based on oracles. Portland State University Library, styczeń 2000. http://dx.doi.org/10.15760/etd.11.

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