Relevant bibliographies by topics / Constraint programming

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Constraint programming'

Author: Grafiati
Published: 4 June 2021
Last updated: 2 November 2024

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Journal articles on the topic "Constraint programming"

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APT, KRZYSZTOF R., and ERIC MONFROY. "Constraint programming viewed as rule-based programming." Theory and Practice of Logic Programming 1, no. 6 (November 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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Van Hentenryck, Pascal, Laurent Michel, and Frédéric Benhamou. "Constraint programming over nonlinear constraints." Science of Computer Programming 30, no. 1-2 (January 1998): 83–118. http://dx.doi.org/10.1016/s0167-6423(97)00008-7.

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O'Sullivan, Barry. "Automated Modelling and Solving in Constraint Programming." Proceedings of the AAAI Conference on Artificial Intelligence 24, no. 1 (July 5, 2010): 1493–97. http://dx.doi.org/10.1609/aaai.v24i1.7530.

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Constraint programming can be divided very crudely into modeling and solving. Modeling defines the problem, in terms of variables that can take on different values, subject to restrictions (constraints) on which combinations of variables are allowed. Solving finds values for all the variables that simultaneously satisfy all the constraints. However, the impact of constraint programming has been constrained by a lack of "user-friendliness''. Constraint programming has a major "declarative" aspect, in that a problem model can be handed off for solution to a variety of standard solving methods. These methods are embedded in algorithms, libraries, or specialized constraint programming languages. To fully exploit this declarative opportunity however, we must provide more assistance and automation in the modeling process, as well as in the design of application-specific problem solvers. Automated modelling and solving in constraint programming presents a major challenge for the artificial intelligence community. Artificial intelligence, and in particular machine learning, is a natural field in which to explore opportunities for moving more of the burden of constraint programming from the user to the machine. This paper presents technical challenges in the areas of constraint model acquisition, formulation and reformulation, synthesis of filtering algorithms for global constraints, and automated solving. We also present the metrics by which success and progress can be measured.
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Dao, Thi-Bich-Hanh, Khanh-Chuong Duong, and Christel Vrain. "Constrained clustering by constraint programming." Artificial Intelligence 244 (March 2017): 70–94. http://dx.doi.org/10.1016/j.artint.2015.05.006.

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SCHRIJVERS, TOM, PETER STUCKEY, and PHILIP WADLER. "Monadic constraint programming." Journal of Functional Programming 19, no. 6 (August 14, 2009): 663–97. http://dx.doi.org/10.1017/s0956796809990086.

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AbstractA constraint programming system combines two essential components: a constraint solver and a search engine. The constraint solver reasons about satisfiability of conjunctions of constraints, and the search engine controls the search for solutions by iteratively exploring a disjunctive search tree defined by the constraint program. In this paper we give a monadic definition of constraint programming in which the solver is defined as a monad threaded through the monadic search tree. We are then able to define search and search strategies as first-class objects that can themselves be built or extended by composable search transformers. Search transformers give a powerful and unifying approach to viewing search in constraint programming, and the resulting constraint programming system is first class and extremely flexible.
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Mattenet, Alex, Ian Davidson, Siegfried Nijssen, and Pierre Schaus. "Generic Constraint-based Block Modeling using Constraint Programming." Journal of Artificial Intelligence Research 70 (February 9, 2021): 597–630. http://dx.doi.org/10.1613/jair.1.12280.

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Block modeling has been used extensively in many domains including social science, spatial temporal data analysis and even medical imaging. Original formulations of the problem modeled it as a mixed integer programming problem, but were not scalable. Subsequent work relaxed the discrete optimization requirement, and showed that adding constraints is not straightforward in existing approaches. In this work, we present a new approach based on constraint programming, allowing discrete optimization of block modeling in a manner that is not only scalable, but also allows the easy incorporation of constraints. We introduce a new constraint filtering algorithm that outperforms earlier approaches, in both constrained and unconstrained settings, for an exhaustive search and for a type of local search called Large Neighborhood Search. We show its use in the analysis of real datasets. Finally, we show an application of the CP framework for model selection using the Minimum Description Length principle.
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Dincbas, M. "Constraint programming." ACM Computing Surveys 28, no. 4es (December 1996): 62. http://dx.doi.org/10.1145/242224.242303.

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Van Hentenryck, Pascal. "Constraint programming." ACM SIGSOFT Software Engineering Notes 25, no. 1 (January 2000): 89–90. http://dx.doi.org/10.1145/340855.341036.

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Van Hentenryck, Pascal. "Constraint Programming." Revue Ouverte d'Intelligence Artificielle 5, no. 2-3 (September 26, 2024): 139–59. http://dx.doi.org/10.5802/roia.76.

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Booth, Kyle E. C., Bryan O'Gorman, Jeffrey Marshall, Stuart Hadfield, and Eleanor Rieffel. "Quantum-accelerated constraint programming." Quantum 5 (September 28, 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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Dissertations / Theses on the topic "Constraint programming"

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Duong, Khanh-Chuong. "Constrained clustering by constraint programming." Thesis, Orléans, 2014. http://www.theses.fr/2014ORLE2049/document.

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La classification non supervisée, souvent appelée par le terme anglais de clustering, est une tâche importante en Fouille de Données. Depuis une dizaine d'années, la classification non supervisée a été étendue pour intégrer des contraintes utilisateur permettant de modéliser des connaissances préalables dans le processus de clustering. Différents types de contraintes utilisateur peuvent être considérés, des contraintes pouvant porter soit sur les clusters, soit sur les instances. Dans cette thèse, nous étudions le cadre de la Programmation par Contraintes (PPC) pour modéliser les tâches de clustering sous contraintes utilisateur. Utiliser la PPC a deux avantages principaux : la déclarativité, qui permet d'intégrer aisément des contraintes utilisateur et la capacité de trouver une solution optimale qui satisfait toutes les contraintes (s'il en existe). Nous proposons deux modèles basés sur la PPC pour le clustering sous contraintes utilisateur. Les modèles sont généraux et flexibles, ils permettent d'intégrer des contraintes d'instances must-link et cannot-link et différents types de contraintes sur les clusters. Ils offrent également à l'utilisateur le choix entre différents critères d'optimisation. Afin d'améliorer l'efficacité, divers aspects sont étudiés. Les expérimentations sur des bases de données classiques et variées montrent qu'ils sont compétitifs par rapport aux approches exactes existantes. Nous montrons que nos modèles peuvent être intégrés dans une procédure plus générale et nous l'illustrons par la recherche de la frontière de Pareto dans un problème de clustering bi-critère sous contraintes utilisateur
Cluster analysis is an important task in Data Mining with hundreds of different approaches in the literature. Since the last decade, the cluster analysis has been extended to constrained clustering, also called semi-supervised clustering, so as to integrate previous knowledge on data to clustering algorithms. In this dissertation, we explore Constraint Programming (CP) for solving the task of constrained clustering. The main principles in CP are: (1) users specify declaratively the problem in a Constraint Satisfaction Problem; (2) solvers search for solutions by constraint propagation and search. Relying on CP has two main advantages: the declarativity, which enables to easily add new constraints and the ability to find an optimal solution satisfying all the constraints (when there exists one). We propose two models based on CP to address constrained clustering tasks. The models are flexible and general and supports instance-level constraints and different cluster-level constraints. It also allows the users to choose among different optimization criteria. In order to improve the efficiency, different aspects have been studied in the dissertation. Experiments on various classical datasets show that our models are competitive with other exact approaches. We show that our models can easily be embedded in a more general process and we illustrate this on the problem of finding the Pareto front of a bi-criterion optimization process
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Achterberg, Tobias. "Constraint integer programming /." München : Verl. Dr. Hut, 2008. http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=017108806&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA.

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Achterberg, Tobias. "Constraint integer programming." München Verl. Dr. Hut, 2007. http://d-nb.info/992163366/04.

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Jefferson, Christopher. "Representations in constraint programming." Thesis, University of York, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.445465.

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McDonald, Iain. "Symmetry in constraint programming." Thesis, University of St Andrews, 2004. http://hdl.handle.net/10023/14983.

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Constraint programming is an invaluable tool for solving many of the complex NP-complete problems that we need solutions to. These problems can be easily described as Constraint Satisfaction Problems (CSPs) and then passed to constraint solvers: complex pieces of software written to solve general CSPs efficiently. Many of the problems we need solutions to are real world problems: planning (e.g. vehicle routing), scheduling (e.g. job shop schedules) and timetabling problems (e.g. staff rotas) to name but a few. In the real world, we place structure on objects to make them easier to deal with. This manifests itself as symmetry. The symmetry in these real world problems make them easier to deal with for humans. However, they lead to a great deal of redundancy when using computational methods of problem solving. Thus, this thesis examines some of the many aspects of utilising the symmetry of CSPs to reduce the amount of computation needed by constraint solvers. In this thesis we look at the ease of use of previous symmetry breaking methods. We introduce a new and novel method of describing the symmetries of CSPs. We look at previous methods of symmetry breaking and show how we can drastically reduce their computation while still breaking all symmetry. We give the first detailed investigation into the behaviour of breaking only subsets of all symmetry. We look at how this affects the performance of constraint solvers before discovering the properties of a good symmetry. We then present an original method for choosing the best symmetries to use. Finally, we look at areas of redundant computation in constraint solvers that no other research has examined. New ways of dealing with this redundancy are proposed with results of an example implementation which improves efficiency by several orders of magnitude.
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Backeman, Peter. "Propagating the nVector Constraint : Haplotype Inference using Constraint Programming." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-211862.

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Genetics research is a wide field and needs computer aid in many different areas. One such problem is the haplotype inference problem by pure parsimony (HIPP). In this thesis the HIPP problem is attacked with a constraint programming (CP) model based on the nVector constraint, for which a new propagator is designed. The results show that the current state-of-the-art  model based on SAT-solvers are in general the most efficient, but that the CP approach in some cases finds a better  solution when time is limited.
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Hnich, Brahim. "Function Variables for Constraint Programming." Doctoral thesis, Uppsala University, Department of Information Science, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-3143.

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Quite often modelers with constraint programming (CP) use the same modelling patterns for different problems, possibly from different domains. This results in recurring idioms in constraint programs. Our approach can be seen as a three-step approach. First, we identify some of these recurring patterns in constraint programs. Second, we propose a general way of describing these patterns by introducing proper constructs that would cover a wide range of applications. Third, we propose automating the process of reproducing these idioms from these higher-level descriptions. The whole process can be seen as a way of encapsulating some of the expertise and knowledge often used by CP modelers and making it available in much simpler forms. Doing so, we are able to extend current CP languages with high-level abstractions that open doors for automation of some of the modelling processes.

In particular, we introduce function variables and allow the statement of constraints on these variables using function operations. A function variable is a decision variable that can take a value from a set of functions as opposed to an integer variable that ranges over integers, or a set variable that ranges over a set of sets. We show that a function variable can be mapped into different representations in terms of integer and set variables, and illustrate how to map constraints stated on a function variable into constraints on integer and set variables. As a result, a function model expressed using function variables opens doors to the automatic generation of alternate CP models. These alternate models either use a different variable representation, or have extra implied constraints, or employ different constraint formulation, or combine different models that are linked using channelling constraints. A number of heuristics are also developed that allow the comparison of different constraint formulations. Furthermore, we present an extensive theoretical comparison of models of injection problems supported by asymptotic and empirical studies. Finally, a practical modelling tool that is built based on a high-level language that allows function variables is presented and evaluated. The tool helps users explore different alternate CP models starting from a function model that is easier to develop, understand, and maintain.

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Jägare, Peter. "Airspace Sectorisation using Constraint Programming." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-155783.

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Given a set of cells and a set of flight routes passing through these cells, we need to cluster cells into a given number of sectors, ensuring an even workload over all sectors, and fulfilling several other constraints on the wellformedness of sectors. The sectorisation is done by using constraint programming. Several propagators are designed to ensure the correctness of the sectorisation.
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Olive, Xavier. "Symmetries in Distributed Constraint Programming." 京都大学 (Kyoto University), 2011. http://hdl.handle.net/2433/142134.

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Wetsel, Gerhard. "Abductive and constraint logic programming." Thesis, Imperial College London, 1997. http://hdl.handle.net/10044/1/7212.

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Books on the topic "Constraint programming"

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Mayoh, Brian, Enn Tyugu, and Jaan Penjam, eds. Constraint Programming. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-642-85983-0.

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Schulte, Christian, ed. Programming Constraint Services. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45945-6.

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Saraswat, Vijay. Concurrent constraint programming. London, England: MIT Press, 1993.

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Achterberg, Tobias. Constraint integer programming. Mu nchen: Verl. Dr. Hut, 2007.

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Apt, Krzysztof R. Principles of constraint programming. Cambridge: Cambridge University Press, 2010.

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Assayag, Gérard, and Charlotte Truchet. Constraint programming in music. Hoboken, NJ: John Wiley & Sons, 2011.

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Frédéric, Benhamou, Jussien Narendra, and O'Sullivan B, eds. Trends in constraint programming. Newport Beach, CA: ISTE USA, 2007.

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Frühwirth, Thom. Essentials of Constraint Programming. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003.

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Benhamou, Frdric, Narendra Jussien, and Barry O'Sullivan, eds. Trends in Constraint Programming. London, UK: ISTE, 2007. http://dx.doi.org/10.1002/9780470612309.

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Hofstedt, Petra. Multiparadigm Constraint Programming Languages. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-17330-1.

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Book chapters on the topic "Constraint programming"

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Borning, Alan, Bjorn Freeman-Benson, and Molly Wilson. "Constraint Hierarchies." In Constraint Programming, 75–115. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-642-85983-0_4.

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Mayoh, Brian, Enn Tyugu, and Tarmo Uustalu. "Constraint Satisfaction and Constraint Programming: A Brief Lead-In." In 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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Palmgren, Erik. "Denotational Semantics of Constraint Logic Programming — A Nonstandard Approach." In Constraint Programming, 261–88. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-642-85983-0_10.

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Mints, Grigori. "Resolution Strategies for the Intuitionistic Logic." In Constraint Programming, 289–311. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-642-85983-0_11.

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Lopez, Gus, Bjorn Freeman-Benson, and Alan Borning. "Kaleidoscope: A Constraint Imperative Programming Language." In Constraint Programming, 313–29. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-642-85983-0_12.

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Penjam, Jaan, and Enn Tyugu. "Constraints in NUT." In Constraint Programming, 330–49. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-642-85983-0_13.

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Hyvőnen, Eero, Stefano De Pascale, and Aarno Lehtola. "Interval Constraint Programming in C++." In Constraint Programming, 350–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-642-85983-0_14.

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Saraswat, Vijay, Radha Jagadeesan, and Vinheet Gupta. "Programming in Timed Concurrent Constraint Languages." In Constraint Programming, 367–413. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-642-85983-0_15.

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Janson, Sverker, and Seif Haridi. "An Introduction to AKL A Multi-Paradigm Programming Language." In Constraint Programming, 414–49. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-642-85983-0_16.

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Mayoh, Brian. "Constraint Programming and Artificial Intelligence." In Constraint Programming, 17–50. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-642-85983-0_2.

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Conference papers on the topic "Constraint programming"

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Roisin, Mathieu, Pierre-Alain Yvars, and Bernard Riera. "Constraint Programming for Logic controller Synthesis." In 2024 10th International Conference on Control, Decision and Information Technologies (CoDIT), 1843–48. IEEE, 2024. http://dx.doi.org/10.1109/codit62066.2024.10708360.

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Karacan, Kübra, Robin Jeanne Kirschner, Hamid Sadeghian, Fan Wu, and Sami Haddadin. "Tactile Robot Programming: Transferring Task Constraints into Constraint-Based Unified Force-Impedance Control." In 2024 IEEE International Conference on Robotics and Automation (ICRA), 204–10. IEEE, 2024. http://dx.doi.org/10.1109/icra57147.2024.10610054.

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Jaffar, J., and J. L. Lassez. "Constraint logic programming." In the 14th ACM SIGACT-SIGPLAN symposium. New York, New York, USA: ACM Press, 1987. http://dx.doi.org/10.1145/41625.41635.

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Jagadeesan, Radha, and Will Marrero. "Timed constraint programming." In the 7th ACM SIGPLAN international conference. New York, New York, USA: ACM Press, 2005. http://dx.doi.org/10.1145/1069774.1069790.

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Di Pierro, Alessandra, and Herbert Wiklicky. "Concurrent constraint programming." In the 2nd ACM SIGPLAN international conference. New York, New York, USA: ACM Press, 2000. http://dx.doi.org/10.1145/351268.351284.

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Saraswat, Vijay A., and Martin Rinard. "Concurrent constraint programming." In the 17th ACM SIGPLAN-SIGACT symposium. New York, New York, USA: ACM Press, 1990. http://dx.doi.org/10.1145/96709.96733.

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Pothitos, Nikolaos, and Panagiotis Stamatopoulos. "Constraint Programming MapReduce'd." In SETN '16: 9th Hellenic Conference on Artificial Intelligence. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2903220.2903248.

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Hemmi, David. "Stochastic Constraint Programming." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/751.

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Combinatorial optimisation problems often contain uncertainty that has to be taken into account to pro- duce realistic solutions. One way of describing the uncertainty is using scenarios, where each sce- nario describes different potential sets of problem parameters based on random distributions or his- torical data. While efficient algorithmic techniques exist for specific problem classes such as linear pro- grams, there are very few approaches that can han- dle general Constraint Programming formulations with uncertainty. The goal of my PhD is to develop generic methods for solving stochastic combina- torial optimisation problems formulated in a Con- straint Programming framework.
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Fang, Cheng. "Efficient Algorithms And Representations For Chance-constrained Mixed Constraint Programming." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/749.

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Resistance to adoption of autonomous systems comes in part from the perceived unreliability of the systems. Concerns can be addressed by approaches that guarantee the probability of success. This is achieved in chance-constrained constraint programming (CC-CP) by imposing constraints required for success, and providing upper-bounds on the probability of violating constraints. This extended abstract reports on novel uncertainty representations to address problems prevalent in current methods.
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Chenouard, Raphaël, Laurent Granvilliers, and Ricardo Soto. "Model-driven constraint programming." In the 10th international ACM SIGPLAN symposium. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1389449.1389479.

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Reports on the topic "Constraint programming"

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Gupta, Nitesh. Constraint Programming Using Multi-Valued Decision Diagrams. Ames (Iowa): Iowa State University, January 2019. http://dx.doi.org/10.31274/cc-20240624-418.

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BROWN UNIV PROVIDENCE RI. Workshop on Principles and Practice of Constraint Programming (1st) Held in Newport, Rhode Island on 28-30 April 1993. Fort Belvoir, VA: Defense Technical Information Center, April 1993. http://dx.doi.org/10.21236/ada281201.

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Ait-Kaci, Hassan, and Andreas Podelski. Position Papers for the First Workshop on Principles and Practice of Constraint Programming Held in Newport, Rhode Island on April 28-30, 1993. Fort Belvoir, VA: Defense Technical Information Center, April 1993. http://dx.doi.org/10.21236/ada281497.

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Ariyawansa, K. A., and Yuntao Zhu. Chance-Constrained Semidefinite Programming. Fort Belvoir, VA: Defense Technical Information Center, January 2000. http://dx.doi.org/10.21236/ada530454.

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Day, William B. Constraints Logic Programming in Knowledge-Based Planning Domains. Fort Belvoir, VA: Defense Technical Information Center, December 1992. http://dx.doi.org/10.21236/ada262958.

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VAN BLOEMEN WAANDERS, BART G., ROSCOE A. BARTLETT, KEVIN R. LONG, PAUL T. BOGGS, and ANDREW G. SALINGER. Large Scale Non-Linear Programming for PDE Constrained Optimization. Office of Scientific and Technical Information (OSTI), October 2002. http://dx.doi.org/10.2172/805833.

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Boggs, P. T., J. W. Tolle, and A. J. Kearsley. A merit function for inequality constrained nonlinear programming problems. Gaithersburg, MD: National Institute of Standards and Technology, 1991. http://dx.doi.org/10.6028/nist.ir.4702.

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Dennis, John E., Tapia Jr., Torczon Richard A., and Virginia J. Some Issues in Nonlinear Programming Algorithms for Problems with Simulation Constraints. Fort Belvoir, VA: Defense Technical Information Center, April 1995. http://dx.doi.org/10.21236/ada294932.

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Thorpe, Jodie, Alisha Ault, Iana Barenboim, Luize Guimarães, Evert-jan Quak, and Katia Taela. Learning from Entrepreneurship Programming for Women’s Economic Empowerment. Institute of Development Studies, June 2023. http://dx.doi.org/10.19088/muva.2023.001.

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Abstract:
MUVA is a social incubator dedicated to developing innovative approaches to the economic empowerment of women in Mozambique. This paper documents experiences from two MUVA projects supporting women’s economic empowerment through entrepreneurship, and draws out broader insights and principles of relevance to other similar programmes. Barriers to women’s economic empowerment and strategies to overcome these barriers are both individual and systemic, visible and invisible. MUVA’s approach to supporting women’s economic empowerment through entrepreneurship involves tailoring three core elements to the specific context of different profiles of women business owners, including urban informal vegetable and fruit traders (MUVA+) and owners of small businesses with untapped growth potential (PAM). Both are groups of low-income female entrepreneurs that are rarely eligible for acceleration and entrepreneurship support. Core programme elements are technical skills, personal development and opportunity generation. However, the project results show that no particular intervention generates impact. Rather, what generates impact is how interventions are tailored to entrepreneurs’ specific business needs, responding to both the external context and internal constraints each group faces, through adapting methodologies that are more often used by formal businesses and policymakers. To achieve this, MUVA bundled interventions in ways that address both visible and invisible barriers and opportunities.
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Sembler, Jose Ignacio, Diether Beuermann, Carlos Elías, and Cheryl Gray. IDB-9: Country Programming. Inter-American Development Bank, March 2013. http://dx.doi.org/10.18235/0010515.

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Abstract:
This paper analyzes whether IDB-9 requirements surrounding the country programming process of the Inter-American Development Bank (IDB, or Bank) are being implemented fully and effectively. The country programming process includes two documents: the Country Strategy, which provides a multiyear overview of the Bank¿s work program; and an annual document that lays out lending allocations and the work program. The main requirements of IDB-9 related to country programming are that Country Strategies include development and macro-fiscal frameworks, that they build on these frameworks and country dialogue to align country programs to country needs, and that they reflect country demand for the Bank¿s lending and nonlending products. The annual programming document is then meant to implement the program laid out in the strategy to ensure that the projects funded by the Bank are in line with country needs. OVE finds that the Bank¿s Country Strategies fulfill some but not all of the IDB-9 mandates. They provide a general description of the characteristics and development challenges in the country, including recent macroeconomic performance, as well as summary diagnoses of sector needs and possible areas of Bank intervention. But they do not generally articulate a strategic approach for the Bank in key sectors or discuss the implications of the macro-fiscal analysis on the role of IDB or the size of IDB lending allocations. They rarely discuss or build on past successes and failures of the Bank in selected areas of intervention, explore the Bank¿s comparative advantage, or fully incorporate relevant analytic work. They provide limited if any information on the NSG lending envelope or portfolio and thereby miss an opportunity to build on potential synergies between SG and NSG instruments. It is only by identifying the synergies between various Bank activities and instruments¿including SG and NSG lending, technical cooperation, and analytic work¿that the Bank can make the most of its resources and tap into its full comparative advantage. With regard to the annual programming process, it is common for projects to be approved and undertaken in sectors that were not envisioned in the Country Strategy, and annual lending allocations do not necessarily accord with the lending envelopes included in Country Strategies. Indeed, the criteria used to determine lending envelopes and annual allocations are not transparent and appear to be closely correlated to past disbursements. In addition, the annual nature of the programming process puts time constraints on loan preparation that hurry the process and lead to year-end bunching of approvals¿and possibly squeeze out time for needed analytic work as well as opportunities for careful discussion and review. In light of these findings, OVE suggests that (i) the Board and Bank Management undertake an in-depth exercise to revisit the Country Strategy guidelines and consider carefully the appropriate role and structure of Country Strategies and Country Program Documents going forward; (ii) the methodology for determining both lending envelopes in country strategies and annual lending allocations in country programs be made more transparent and the Operational Program Report presented to the Board show how those annual allocations relate to IDB-9 priorities and country needs; and (iii) the programming process be carried out on a rolling two-year basis (with the first year being binding and the second year showing notional allocations and work programs) to allow greater time for planning and executing loans and other Bank support.
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