Relevant bibliographies by topics / Constraint propagation

Contents

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

Academic literature on the topic 'Constraint propagation'

Author: Grafiati
Published: 4 June 2021
Last updated: 26 July 2025

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Journal articles on the topic "Constraint propagation"

1

Cohen, David, Christopher Jefferson, and Karen Petrie. "A Theoretical Framework for Constraint Propagator Triggering." Proceedings of the International Symposium on Combinatorial Search 7, no. 1 (2021): 19–27. http://dx.doi.org/10.1609/socs.v7i1.18388.

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CSP instances are commonly solved by backtracking search combined with constraint propagation. During search, constraint solvers aim to remove any literals (variable-value pair) that can be shown not to be part of any solution. This literal removal, called propagation, is the beating heart of modern constraint solvers. A significant proportion of the runtime of propagating constraint solvers is spent running propagation algorithms. Therefore any mechanism for reducing how frequently propagators are called leads directly to significant performance improvements. One family of popular techniques
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Domes, Ferenc, and Arnold Neumaier. "Constraint propagation on quadratic constraints." Constraints 15, no. 3 (2009): 404–29. http://dx.doi.org/10.1007/s10601-009-9076-1.

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Chu, Wesley W., and Patrick H. Ngai. "Embedding temporal constraint propagation in machine sequencing for job shop scheduling." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 7, no. 1 (1993): 37–52. http://dx.doi.org/10.1017/s0890060400000056.

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In this paper, we show how a temporal constraint propagation technique can be embedded in the machine sequencing approach for solving the job shop scheduling problem. The temporal constraint propagation algorithm propagates the precedence constraints and machine interference constraints to reduce the search space generated by the machine sequencing approach. Further, by making use of the temporal nature of the job shop scheduling, efficient algorithms to propagate precedence constraints and machine interference constraints are developed. Experimental results reveal that embedding constraint pr
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Habib, Walid E., and Allen C. Ward. "Causality in constraint propagation." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 11, no. 5 (1997): 419–33. http://dx.doi.org/10.1017/s0890060400003346.

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AbstractThis paper defines, for use in design, rules for propagating “distribution constraints” through relationships such as algebraic or vector equations. Distribution constraints are predicate logic statements about the values that physical system parameters may assume. The propagation rules take into account “variation source causality”: information about when and how the values are assigned during the design, manufacturing, and operation of the system.
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ABDENNADHER, SLIM, and CHRISTOPHE RIGOTTI. "AUTOMATIC GENERATION OF RULE-BASED SOLVERS FOR INTENTIONALLY DEFINED CONSTRAINTS." International Journal on Artificial Intelligence Tools 11, no. 02 (2002): 283–302. http://dx.doi.org/10.1142/s0218213002000903.

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A general approach to implement propagation and simplification of constraints consists of applying rules over these constraints. However, a difficulty that arises frequently when writing a constraint solver is to determine the constraint propagation algorithm. In previous work, different methods for automatic generation of rule-based solvers for constraints defined over finite domains have been proposed1,2,3,4. In this paper, we present a method for generating rule-based solvers for constraint predicates defined by means of a constraint logic program, even when the constraint domain is infinit
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Fu, Zhenyong, Zhiwu Lu, Horace Ip, Yuxin Peng, and Hongtao Lu. "Symmetric Graph Regularized Constraint Propagation." Proceedings of the AAAI Conference on Artificial Intelligence 25, no. 1 (2011): 350–55. http://dx.doi.org/10.1609/aaai.v25i1.7897.

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This paper presents a novel symmetric graph regularization framework for pairwise constraint propagation. We first decompose the challenging problem of pairwise constraint propagation into a series of two-class label propagation subproblems and then deal with these subproblems by quadratic optimization with symmetric graph regularization. More importantly, we clearly show that pairwise constraint propagation is actually equivalent to solving a Lyapunov matrix equation, which is widely used in Control Theory as a standard continuous-time equation. Different from most previous constraint propaga
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Sideris, Andreas, and Yannis Dimopoulos. "Constraint Propagation in Propositional Planning." Proceedings of the International Conference on Automated Planning and Scheduling 20 (May 25, 2021): 153–60. http://dx.doi.org/10.1609/icaps.v20i1.13422.

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Planning as Satisfiability is a most successful approach to optimal propositional planning. It draws its strength from the efficiency of state-of-the-art propositional satisfiability solvers, combined with the utilization of constraints that are inferred from the problem planning graph. One of the recent improvements of the framework is the addition of long-distance mutual exclusion (londex) constraints that relate facts and actions which refer to different time steps. In this paper we compare different encodings of planning as satisfiability wrt the constraint propagation they achieve in a mo
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Metodi, A., M. Codish, and P. J. Stuckey. "Boolean Equi-propagation for Concise and Efficient SAT Encodings of Combinatorial Problems." Journal of Artificial Intelligence Research 46 (March 6, 2013): 303–41. http://dx.doi.org/10.1613/jair.3809.

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We present an approach to propagation-based SAT encoding of combinatorial problems, Boolean equi-propagation, where constraints are modeled as Boolean functions which propagate information about equalities between Boolean literals. This information is then applied to simplify the CNF encoding of the constraints. A key factor is that considering only a small fragment of a constraint model at one time enables us to apply stronger, and even complete, reasoning to detect equivalent literals in that fragment. Once detected, equivalences apply to simplify the entire constraint model and facilitate f
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Nightingale, P., I. P. Gent, C. Jefferson, and I. Miguel. "Short and Long Supports for Constraint Propagation." Journal of Artificial Intelligence Research 46 (January 17, 2013): 1–45. http://dx.doi.org/10.1613/jair.3749.

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Special-purpose constraint propagation algorithms frequently make implicit use of short supports -- by examining a subset of the variables, they can infer support (a justification that a variable-value pair may still form part of an assignment that satisfies the constraint) for all other variables and values and save substantial work -- but short supports have not been studied in their own right. The two main contributions of this paper are the identification of short supports as important for constraint propagation, and the introduction of HaggisGAC, an efficient and effective general purpose
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Monfroy, Eric. "Control-driven constraint propagation." Applied Artificial Intelligence 15, no. 1 (2001): 79–103. http://dx.doi.org/10.1080/08839510150204626.

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Dissertations / Theses on the topic "Constraint propagation"

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Lagerkvist, Mikael Zayenz. "Techniques for Efficient Constraint Propagation." Licentiate thesis, KTH, Electronic, Computer and Software Systems, ECS, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-9511.

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<p>This thesis explores three new techniques for increasing the efficiency of constraint propagation: support for incremental propagation, improved representation of constraints, and abstractions to simplify propagation.  Support for incremental propagation is added to a propagator centered propagation system by adding a new intermediate layer of abstraction, advisors, that capture the essential aspects of a variable centered system. Advisors are used to give propagators a detailed view of the dynamic changes between propagator runs. Advisors enable the implementation of optimal algorithms for
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Gennari, Rosella. "Mapping Inferences: Constraint Propagation and Diamond Satisfaction." Diss., Universiteit van Amsterdam, 2002. http://hdl.handle.net/10919/71553.

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The main theme shared by the two main parts of this thesis is EFFICIENT AUTOMATED REASONING.Part I is focussed on a general theory underpinning a number of efficient approximate algorithms for Constraint Satisfaction Problems (CSPs),the constraint propagation algorithms.In Chapter 3, we propose a Structured Generic Algorithm schema (SGI) for these algorithms. This iterates functions according to a certain strategy, i.e. by searching for a common fixpoint of the functions. A simple theory for SGI is developed by studying properties of functions and of the ways these influence the basic strategy
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Chen, Kenway. "MCAD - ECAD integration : constraint modeling and propagation /." Thesis, Atlanta, Ga. : Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/26484.

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Thesis (M. S.)--Mechanical Engineering, Georgia Institute of Technology, 2009.<br>Committee Chair: Schaefer, Dirk; Committee Member: Panchal, Jitesh; Committee Member: Paredis, Chris; Committee Member: Rosen, David; Committee Member: Yoder, Douglas. Part of the SMARTech Electronic Thesis and Dissertation Collection.
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Srinivasa, Raghavan Aravind. "Improving support for generic programming in C# with associated types and constraint propagation." [College Station, Tex. : Texas A&M University, 2007. http://hdl.handle.net/1969.1/ETD-TAMU-1273.

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Scott, Joseph. "Other Things Besides Number : Abstraction, Constraint Propagation, and String Variable Types." Doctoral thesis, Uppsala universitet, Avdelningen för datalogi, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-273311.

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Constraint programming (CP) is a technology in which a combinatorial problem is modeled declaratively as a conjunction of constraints, each of which captures some of the combinatorial substructure of the problem. Constraints are more than a modeling convenience: every constraint is partially implemented by an inference algorithm, called a propagator, that rules out some but not necessarily all infeasible candidate values of one or more unknowns in the scope of the constraint. Interleaving propagation with systematic search leads to a powerful and complete solution method, combining a high degr
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Palavalasa, Swetha Rao. "Implementation of Constraint Propagation Tree for Question Answering Systems." Available to subscribers only, 2009. http://proquest.umi.com/pqdweb?did=1796121021&sid=6&Fmt=2&clientId=1509&RQT=309&VName=PQD.

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Sadoune, Michel. "Terminal area flight path generation using parallel constraint propagation." Thesis, Massachusetts Institute of Technology, 1989. http://hdl.handle.net/1721.1/40136.

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Woodward, Robert J. "Les cohérences fortes : où, quand, et combien." Thesis, Montpellier, 2018. http://www.theses.fr/2018MONTS145/document.

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Déterminer si un problème de satisfaction de contraintes (CSP) a une solution ou non est NP-complet. Les CSP sont résolus par inférence (c’est-à-dire, en appliquant un algorithme de cohérence), par énumération (c’est-à-dire en effectuant une recherche avec retour sur trace ou backtracking), ou, plus souvent, en intercalant les deux mécanismes. La propriété de cohérence la plus courante appliquée en cours du backtracking est la GAC (Generalized Arc Consistency). Au cours des dernières années, de nouveaux algorithmes pour appliquer des cohérences plus fortes que le GAC ont été proposés et montré
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Tambouratzis, Tatiani. "A neural network implementation of the constraint propagation paradigm in vision." Thesis, Brunel University, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.290953.

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Tu, Ronghui. "Generalized Survey Propagation." Thèse, Université d'Ottawa / University of Ottawa, 2011. http://hdl.handle.net/10393/19972.

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Survey propagation (SP) has recently been discovered as an efficient algorithm in solving classes of hard constraint-satisfaction problems (CSP). Powerful as it is, SP is still a heuristic algorithm, and further understanding its algorithmic nature, improving its effectiveness and extending its applicability are highly desirable. Prior to the work in this thesis, Maneva et al. introduced a Markov Random Field (MRF) formalism for k-SAT problems, on which SP may be viewed as a special case of the well-known belief propagation (BP) algorithm. This result had sometimes been interpreted to an unde
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Books on the topic "Constraint propagation"

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Huy, Toàn Phan. Constraint Propagation in Flexible Manufacturing. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-58335-3.

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Davis, Ernest. Constraint propagation on real-valued quantities. Courant Institute of Mathematical Sciences, New York University, 1985.

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tutkimuskeskus, Valtion teknillinen, ed. Constraint reasoning with incomplete knowledge: The tolerance propagation approach. Technical Research Centre of Finland, 1991.

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Tambouratzis, Tatiani. A neural network implementation of the constraint propagation paradigm in vision. Brunel University, 1991.

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Rogerson, G. A. Aspects of wave propagation in constrained and nearly constrained elastic bodies. University of East Anglia, 1987.

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Cordes, James M. Constraints on cosmic ray propagation in the galaxy: Final report for NASA grant NAGW-1615. National Aeronautics and Space Administration, 1992.

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R, Reilinger, United States. National Aeronautics and Space Administration., and California Institute of Technology, eds. Age constraints for the present fault configuration in the Imperial Valley, California: Evidence for northwestward propagation of the Gulf of California rift system. California Institute of Technology, 1990.

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R, Reilinger, United States. National Aeronautics and Space Administration., and California Institute of Technology, eds. Age constraints for the present fault configuration in the Imperial Valley, California: Evidence for northwestward propagation of the Gulf of California rift system. California Institute of Technology, 1990.

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United States. National Aeronautics and Space Administration., ed. Study of one- and two-dimensional filtering and deconvolution algorithms for a streaming array computer: Final report. National Aeronautics and Space Administration, 1985.

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United States. National Aeronautics and Space Administration., ed. Study of one- and two-dimensional filtering and deconvolution algorithms for a streaming array computer: Final report, appendix 5. National Aeronautics and Space Administration, 1985.

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Book chapters on the topic "Constraint propagation"

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Dorndorf, Ulrich. "Constraint Propagation." In Project Scheduling with Time Windows. Physica-Verlag HD, 2002. http://dx.doi.org/10.1007/978-3-642-57506-8_3.

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Sellmann, Meinolf, and Warwick Harvey. "Heuristic Constraint Propagation." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-46135-3_55.

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Utgoff, Paul E. "Constraint Back-Propagation." In The Kluwer International Series in Engineering and Computer Science. Springer US, 1986. http://dx.doi.org/10.1007/978-1-4613-2283-2_6.

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Baptiste, Philippe, Claude Le Pape, and Wim Nuijten. "Propagation of Cumulative Constraints." In Constraint-Based Scheduling. Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-1479-4_3.

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Baptiste, Philippe, Claude Le Pape, and Wim Nuijten. "Comparison of Propagation Techniques." In Constraint-Based Scheduling. Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-1479-4_4.

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Baptiste, Philippe, Claude Le Pape, and Wim Nuijten. "Propagation of Objective Functions." In Constraint-Based Scheduling. Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-1479-4_5.

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Matiyasevich, Yuri. "Interval Computations as Propagation of Constraints." In Constraint Programming. Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-642-85983-0_6.

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He, Ping, Xiaohua Xu, and Ling Chen. "Constrained Clustering with Local Constraint Propagation." In Computer Vision – ECCV 2012. Workshops and Demonstrations. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33885-4_23.

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Schulte, Christian, and Peter J. Stuckey. "Speeding Up Constraint Propagation." In Principles and Practice of Constraint Programming – CP 2004. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30201-8_45.

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Harvey, Warwick, and Peter J. Stuckey. "Constraint Representation for Propagation." In Principles and Practice of Constraint Programming — CP98. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/3-540-49481-2_18.

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Conference papers on the topic "Constraint propagation"

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Ratzke, Axel, Sebastian Post, Johannes Koch, and Christoph Grimm. "Constructive Model Analysis of SysMLv2 Models by Constraint Propagation." In 2024 19th Annual System of Systems Engineering Conference (SoSE). IEEE, 2024. http://dx.doi.org/10.1109/sose62659.2024.10620947.

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Zhou, Lingfeng, Shanlin Xiao, Huiyao Wang, et al. "An Efficient Asynchronous Circuits Design Flow with Backward Delay Propagation Constraint." In 2024 Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE, 2024. http://dx.doi.org/10.23919/date58400.2024.10546503.

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Lu, Zhiwu, and Yuxin Peng. "Heterogeneous Constraint Propagation with Constrained Sparse Representation." In 2012 IEEE 12th International Conference on Data Mining (ICDM). IEEE, 2012. http://dx.doi.org/10.1109/icdm.2012.13.

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Francis, Kathryn, and Peter J. Stuckey. "Constraint propagation for loose constraint graphs." In the 2007 ACM symposium. ACM Press, 2007. http://dx.doi.org/10.1145/1244002.1244081.

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Latour, Anna Louise D., Behrouz Babaki, and Siegfried Nijssen. "Stochastic Constraint Propagation for Mining Probabilistic Networks." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. 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 pro
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Zhu, Xiatian, Chen Change Loy, and Shaogang Gong. "Constrained Clustering: Effective Constraint Propagation with Imperfect Oracles." In 2013 IEEE International Conference on Data Mining (ICDM). IEEE, 2013. http://dx.doi.org/10.1109/icdm.2013.45.

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Martinez, Thierry, François Fages, and Sylvain Soliman. "Search by constraint propagation." In PPDP '15: 17th International Symposium on Principles and Practice of Declarative Programming. ACM, 2015. http://dx.doi.org/10.1145/2790449.2790527.

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Sarma, Sanjay E., and James R. Rinderle. "Quiescence in Internal Propagation." In ASME 1991 Design Technical Conferences. American Society of Mechanical Engineers, 1991. http://dx.doi.org/10.1115/detc1991-0057.

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Abstract Concurrent design often involves a large number of variables related through a complex network of constraints. This not only makes it difficult to find a design solution, but also to understand the design parameter trade-offs, trends and interactions. Precise estimates of all the design parameters which would facilitate the exploration of tradeoffs and interactions are generally not available, however, the allowable ranges or intervals of some design parameters are often known. Narrower, or more refined intervals, facilitate the inference of stronger conclusions and the more robust ap
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Jefferson, Christopher. "Modern constraint solving by propagation." In 2011 9th IEEE/ACM International Conference on Formal Methods and Models for Codesign (MEMOCODE 2011). IEEE, 2011. http://dx.doi.org/10.1109/memcod.2011.5970520.

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Liu, Hui, Yuheng Jia, Junhui Hou, and Qingfu Zhang. "Imbalance-aware Pairwise Constraint Propagation." In MM '19: The 27th ACM International Conference on Multimedia. ACM, 2019. http://dx.doi.org/10.1145/3343031.3350968.

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Reports on the topic "Constraint propagation"

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Tasdemirci, Alper, Ian W. Hall, Bazle A. Gama, and Mustafa Guden. The Effects of Layer Constraint on Stress Wave Propagation in Multilayer Composite Materials. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada427963.

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Vecherin, Sergey N., D. K. Wilson, and Chris L. Pettit. Optimal Sensor Placement with Terrain-Based Constraints and Signal Propagation Effects. Defense Technical Information Center, 2008. http://dx.doi.org/10.21236/ada494571.

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Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, 1996. http://dx.doi.org/10.32747/1996.7613033.bard.

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The objectives of this project were to develop procedures and models, based on neural networks, for quality sorting of agricultural produce. Two research teams, one in Purdue University and the other in Israel, coordinated their research efforts on different aspects of each objective utilizing both melons and tomatoes as case studies. At Purdue: An expert system was developed to measure variances in human grading. Data were acquired from eight sensors: vision, two firmness sensors (destructive and nondestructive), chlorophyll from fluorescence, color sensor, electronic sniffer for odor detecti
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