Relevant bibliographies by topics / Constraints satisfaction problem

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  1. Journal articles
  2. Dissertations / Theses
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  4. Book chapters
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Academic literature on the topic 'Constraints satisfaction problem'

Author: Grafiati
Published: 4 June 2021
Last updated: 8 June 2024

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Journal articles on the topic "Constraints satisfaction problem"

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Frank, Jeremy. "Revisiting dynamic constraint satisfaction for model-based planning." Knowledge Engineering Review 31, no. 5 (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 classificati
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Zuenko, Alexander A., Olga V. Fridman, and Olga N. Zuenko. "An approach to finding a global optimum in constrained clustering tasks involving the assessments of several experts." Transaction Kola Science Centre 12, no. 5-2021 (2021): 75–90. http://dx.doi.org/10.37614/2307-5252.2021.5.12.007.

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An approach to solving the constrained clustering problem has been developed, based on the aggregation of data obtained as a result of evaluating the characteristics of clustered objects by several independent experts, and the analysis of alternative variants of clustering by constraint programming methods using original heuristics. Objects clusterized are represented as multisets, which makes it possible to use appropriate methods of aggregation of expert opinions. It is proposed to solve the constrained clustering problem as a constraint satisfaction problem. The main attention is paid to th
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Vakhnin, Aleksei, and Zakhar Novikov. "Using cooperative coevolution in large-scale black-box constraint satisfaction problems." ITM Web of Conferences 59 (2024): 02022. http://dx.doi.org/10.1051/itmconf/20245902022.

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Solving constrained large-scale global optimization problems poses a challenging task. In these problems with constraints, when the number of variables is measured in the thousands, when the constraints are presented in the form of a black box, and neither the size nor the configuration of the feasible region is known, it is very difficult to find at least one feasible solution. In general, such a problem of finding a feasible region is known as a constraint satisfaction problem. In this paper, we have extended a well-known benchmark set based on constrained optimization problems up to 1000 va
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De Haan, Ronald, Iyad Kanj, and Stefan Szeider. "On the Subexponential-Time Complexity of CSP." Journal of Artificial Intelligence Research 52 (January 30, 2015): 203–34. http://dx.doi.org/10.1613/jair.4540.

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Not all NP-complete problems share the same practical hardness with respect to exact computation. Whereas some NP-complete problems are amenable to efficient computational methods, others are yet to show any such sign. It becomes a major challenge to develop a theoretical framework that is more fine-grained than the theory of NP-completeness, and that can explain the distinction between the exact complexities of various NP-complete problems. This distinction is highly relevant for constraint satisfaction problems under natural restrictions, where various shades of hardness can be observed in p
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Hou, Dong-liang, and Fang-rong Chen. "Constraint Satisfaction Technology for Stacking Problem with Ordered Constraints." Procedia Engineering 29 (2012): 3317–21. http://dx.doi.org/10.1016/j.proeng.2012.01.487.

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Gao, Yan, Xin Zhang, and Jian Zhong Xu. "An Improved Production Scheduling Algorithm Based on Resource Constraints." Applied Mechanics and Materials 455 (November 2013): 619–24. http://dx.doi.org/10.4028/www.scientific.net/amm.455.619.

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For resource-constrained project scheduling problems, with aircraft assembly as its background, we established its mathematics model as constraint satisfaction problem. An improved critical path scheduling algorithm is proposed, considering the constraints of precedence relations, resource constraints and space constraints, through the two stages of planning, reaching for aircraft assembly task scheduling optimization objectives. Through the given numerical example results show that, when the objective consists in minimizing the project duration, the algorithm has better performance.
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Helzerman, R. A., and M. P. Harper. "MUSE CSP: An Extension to the Constraint Satisfaction Problem." Journal of Artificial Intelligence Research 5 (November 1, 1996): 239–88. http://dx.doi.org/10.1613/jair.298.

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This paper describes an extension to the constraint satisfaction problem (CSP) called MUSE CSP (MUltiply SEgmented Constraint Satisfaction Problem). This extension is especially useful for those problems which segment into multiple sets of partially shared variables. Such problems arise naturally in signal processing applications including computer vision, speech processing, and handwriting recognition. For these applications, it is often difficult to segment the data in only one way given the low-level information utilized by the segmentation algorithms. MUSE CSP can be used to compactly repr
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Baykan, Can A., and Mark S. Fox. "Spatial synthesis by disjunctive constraint satisfaction." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 11, no. 4 (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, ad
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Bodirsky, M., and M. Hils. "Tractable Set Constraints." Journal of Artificial Intelligence Research 45 (December 31, 2012): 731–59. http://dx.doi.org/10.1613/jair.3747.

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Many fundamental problems in artificial intelligence, knowledge representation, and verification involve reasoning about sets and relations between sets and can be modeled as set constraint satisfaction problems (set CSPs). Such problems are frequently intractable, but there are several important set CSPs that are known to be polynomial-time tractable. We introduce a large class of set CSPs that can be solved in quadratic time. Our class, which we call EI, contains all previously known tractable set CSPs, but also some new ones that are of crucial importance for example in description logics.
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Gorti, Sreenivasa Rao, Salal Humair, Ram D. Sriram, Sarosh Talukdar, and Sesh Murthy. "Solving constraint satisfaction problems using ATeams." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 10, no. 1 (1996): 1–19. http://dx.doi.org/10.1017/s0890060400001256.

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AbstractThis paper presents an approach to solving constraint satisfaction problems using Asynchronous Teams of autonomous agents (ATeams). The focus for the constraint satisfaction problem is derived from an effort to support spatial layout generation in a conceptual design framework. The constraint specification allows a high-level representation and manipulation of qualitative geometric information. We present a computational technique based on ATeams to instantiate solutions to the constraint satisfaction problem. The technique uses a search for a solution in numerical space. This permits
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Dissertations / Theses on the topic "Constraints satisfaction problem"

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

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Malibary, Areej. "Addressing Supply Chain Management as a Distributed Multi-Layered Constraints Satisfaction Problem." Thesis, University of Essex, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.520096.

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Karakoc, Erman. "Web Service Composition Under Resource Allocation Constraints." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/12608309/index.pdf.

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Web service composition is an inevitable aspect of web services technology, which solves complex problems by combining available basic services and ordering them to best suit the problem requirements. Automatic composition gives us flexibility of selecting best candidate services at composition time, this would require the user to define the resource allocation constraints for selecting and composing candidate web services. Resource allocation constraints define restrictions on how to allocate resources, and scheduling under resource allocation constraints to provide proper resource allocation
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Sadeg, Lamia. "Méthodes de décomposition pour la résolution des PCSP (Partial Constraint Satisfaction Problem) : application aux problèmes FAP et coloration de graphes." Thesis, Université de Lorraine, 2016. http://www.theses.fr/2016LORR0314.

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Les applications réelles liées aux problèmes de satisfaction partielle de contraintes (PCSP : Partial Constraints Satisfaction Problem) sont de plus en plus nombreuses, ce qui justifie l’intérêt croissant des chercheurs pour cette classe de problèmes. La résolution d’un PCSP revient à affecter des valeurs à toutes ses variables tout en maximisant (ou minimisant) une fonction objectif prédéfinie. Ces problèmes sont NP-difficiles, par conséquent il n’existe aucune approche aussi bien exacte qu’heuristique efficace sur les grandes instances. Pour résoudre efficacement les instances difficiles, un
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Sadeg, Lamia. "Méthodes de décomposition pour la résolution des PCSP (Partial Constraint Satisfaction Problem) : application aux problèmes FAP et coloration de graphes." Electronic Thesis or Diss., Université de Lorraine, 2016. http://www.theses.fr/2016LORR0314.

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Les applications réelles liées aux problèmes de satisfaction partielle de contraintes (PCSP : Partial Constraints Satisfaction Problem) sont de plus en plus nombreuses, ce qui justifie l’intérêt croissant des chercheurs pour cette classe de problèmes. La résolution d’un PCSP revient à affecter des valeurs à toutes ses variables tout en maximisant (ou minimisant) une fonction objectif prédéfinie. Ces problèmes sont NP-difficiles, par conséquent il n’existe aucune approche aussi bien exacte qu’heuristique efficace sur les grandes instances. Pour résoudre efficacement les instances difficiles, un
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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 const
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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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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 cons
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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
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Books on the topic "Constraints satisfaction problem"

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Ghédira, Khaled, and Bernard Dubuisson, eds. Constraint Satisfaction Problems. John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118574522.

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A. S. Janet van der Linden. Dynamic meta-constraints: An approach to dealing with non-standard constraint satisfaction problems. Oxford Brookes University, 2000.

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Živný, Stanislav. The Complexity of Valued Constraint Satisfaction Problems. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33974-5.

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Creignou, Nadia. Complexity classifications of Boolean constraint satisfaction problems. Society for Industrial and Applied Mathematics, 2001.

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The complexity of valued constraint satisfaction problems. Springer, 2012.

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

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Wahbi, Mohamed. Algorithms and Ordering Heuristics for Distributed Constraint Satisfaction Problems. John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118753620.

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

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Kubica, Bartłomiej Jacek. Interval Methods for Solving Nonlinear Constraint Satisfaction, Optimization and Similar Problems. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13795-3.

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Nachtsheim, Philip R. A technique for solving constraint satisfaction problems using Prolog's definite clause grammars. National Aeronautics and Space Administration, Ames Research Center, 1989.

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Book chapters on the topic "Constraints satisfaction problem"

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Unno, Hiroshi, Tachio Terauchi, and Eric Koskinen. "Constraint-Based Relational Verification." In Computer Aided Verification. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81685-8_35.

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AbstractIn recent years they have been numerous works that aim to automate relational verification. Meanwhile, although Constrained Horn Clauses ($$\mathrm {CHCs}$$ CHCs ) empower a wide range of verification techniques and tools, they lack the ability to express hyperproperties beyond k-safety such as generalized non-interference and co-termination.This paper describes a novel and fully automated constraint-based approach to relational verification. We first introduce a new class of predicate Constraint Satisfaction Problems called $$\mathrm {pfwCSP}$$ pfwCSP where constraints are represented as clauses modulo first-order theories over predicate variables of three kinds: ordinary, well-founded, or functional. This generalization over $$\mathrm {CHCs}$$ CHCs permits arbitrary (i.e., possibly non-Horn) clauses, well-foundedness constraints, functionality constraints, and is capable of expressing these relational verification problems. Our approach enables us to express and automatically verify problem instances that require non-trivial (i.e., non-sequential and non-lock-step) self-composition by automatically inferring appropriate schedulers (or alignment) that dictate when and which program copies move. To solve problems in this new language, we present a constraint solving method for $$\mathrm {pfwCSP}$$ pfwCSP based on stratified CounterExample-Guided Inductive Synthesis (CEGIS) of ordinary, well-founded, and functional predicates.We have implemented the proposed framework and obtained promising results on diverse relational verification problems that are beyond the scope of the previous verification frameworks.
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Yokoo, Makoto. "Constraint Satisfaction Problem." In Distributed Constraint Satisfaction. 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." In Distributed Constraint Satisfaction. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-59546-2_2.

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Ghédira, Khaled, and Bernard Dubuisson. "Foundations of CSP." In Constraint Satisfaction Problems. John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118574522.ch1.

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Ghédira, Khaled, and Bernard Dubuisson. "Consistency Reinforcement Techniques." In Constraint Satisfaction Problems. John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118574522.ch2.

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Ghédira, Khaled, and Bernard Dubuisson. "CSP Solving Algorithms." In Constraint Satisfaction Problems. John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118574522.ch3.

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Ghédira, Khaled, and Bernard Dubuisson. "Search Heuristics." In Constraint Satisfaction Problems. John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118574522.ch4.

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Ghédira, Khaled, and Bernard Dubuisson. "Learning Techniques." In Constraint Satisfaction Problems. John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118574522.ch5.

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Ghédira, Khaled, and Bernard Dubuisson. "Maximal Constraint Satisfaction Problems." In Constraint Satisfaction Problems. John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118574522.ch6.

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Ghédira, Khaled, and Bernard Dubuisson. "Constraint Satisfaction and Optimization Problems." In Constraint Satisfaction Problems. John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118574522.ch7.

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Conference papers on the topic "Constraints satisfaction problem"

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Marsden, Gary C., F. Kiamilev, S. Esener, and Sing H. Lee. "Optical Matrix Encoding for Constraint Satisfaction." In Optical Computing. 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 solution
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Vardi, Moshe Y., and Zhiwei Zhang. "Solving Quantum-Inspired Perfect Matching Problems via Tutte-Theorem-Based Hybrid Boolean Constraints." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/227.

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Determining the satisfiability of Boolean constraint-satisfaction problems with different types of constraints, that is hybrid constraints, is a well-studied problem with important applications. We study a new application of hybrid Boolean constraints, which arises in quantum computing. The problem relates to constrained perfect matching in edge-colored graphs. While general-purpose hybrid constraint solvers can be powerful, we show that direct encodings of the constrained-matching problem as hybrid constraints scale poorly and special techniques are still needed. We propose a novel encoding b
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Rangavajhala, Sirisha, Anoop A. Mullur, and Achille Messac. "Equality Constraints in Multiobjective Robust Design Optimization: Implications and Tradeoffs." In ASME 2005 International Mechanical Engineering Congress and Exposition. ASMEDC, 2005. http://dx.doi.org/10.1115/imece2005-79961.

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In the present paper, we explore issues in handling equality constraints in multiobjective robust design optimization (RDO) problems. Satisfying an equality constraint exactly under uncertainty can be a challenging task. The challenge of handling equality constraints is compounded in multiobjective RDO problems. Modeling the tradeoffs between the mean of the performance and the variation of the performance for each design objective in a multiobjective RDO problem is a complex task by itself. Equality constraints add to this complexity because of the additional tradeoffs that are introduced bet
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Alhaijawy, Feras Sameer, and Adina Magda Florea. "Scheduling People's Daily Activities Using Temporal Constraints Satisfaction Problem." In 2018 5th International Conference on Control, Decision and Information Technologies (CoDIT). IEEE, 2018. http://dx.doi.org/10.1109/codit.2018.8394957.

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D’Ambrosio, Joseph, Timothy Darr, and William Birmingham. "A Constraint Satisfaction Approach for Multi-Attribute Design Optimization Problems." In ASME 1997 Design Engineering Technical Conferences. American Society of Mechanical Engineers, 1997. http://dx.doi.org/10.1115/detc97/dtm-3887.

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Abstract In this paper, we describe a multi-attribute domain CSP approach for solving a class of discrete, constrained, optimization problems. The multi-attribute domain CSP formulation provides a compact representation for design problems characterized by multiple, conflicting attributes. Design trade-off information is represented by a multi-attribute value function. Necessary conditions for an optimal solution, defined in terms of the value function, are represented as constraints. This provides a uniform problem-solving approach (constraint satisfaction) for identifying solutions that are
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Zhang, Linrui, Li Shen, Long Yang, et al. "Penalized Proximal Policy Optimization for Safe Reinforcement Learning." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/520.

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Safe reinforcement learning aims to learn the optimal policy while satisfying safety constraints, which is essential in real-world applications. However, current algorithms still struggle for efficient policy updates with hard constraint satisfaction. In this paper, we propose Penalized Proximal Policy Optimization (P3O), which solves the cumbersome constrained policy iteration via a single minimization of an equivalent unconstrained problem. Specifically, P3O utilizes a simple yet effective penalty approach to eliminate cost constraints and removes the trust-region constraint by the clipped s
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Shanmugavelu, Ilango, Albert Esterline, Donald R. Riley, and Arthur G. Erdman. "An Opportunistic Approach to Conceptual Design." In ASME 1991 International Computers in Engineering Conference and Exposition. American Society of Mechanical Engineers, 1991. http://dx.doi.org/10.1115/cie1991-0016.

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Abstract This paper addresses automation of the conceptual design stage of the overall design process, the stage that contributes the most to the cost and quality of the final product. An opportunistic constraint-based approach to conceptual design is proposed here. This approach combines some important aspects of the constraint-based transformational approach (for strongly connected systems) with aspects of the decompositional approach (for weakly connected systems) through the ability to change focus from one component to another depending on the available information and the ability to refi
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Haney, M. W., R. A. Athale, and R. A. Geesey. "Use of optical techniques in the consistent labeling problem." In OSA Annual Meeting. Optica Publishing Group, 1987. http://dx.doi.org/10.1364/oam.1987.tuw1.

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Many artificial intelligence problems are formulated as backtracking tree searches. In general, these problems have exponential time complexity, and many researchers have concentrated on developing practical tree-pruning techniques to improve the efficiency of the search algorithm. Consistent labeling (CL) techniques (variously known as constraint satisfaction and forward checking techniques) use the relational constraints of the problem in local graph operations to reduce the complexity of the search tree. The appropriate data structures for some CL operations are Boolean tables which can be
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HasanzadeZonuzy, Aria, Dileep Kalathil, and Srinivas Shakkottai. "Model-Based Reinforcement Learning for Infinite-Horizon Discounted Constrained Markov Decision Processes." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/347.

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In many real-world reinforcement learning (RL) problems, in addition to maximizing the objective, the learning agent has to maintain some necessary safety constraints. We formulate the problem of learning a safe policy as an infinite-horizon discounted Constrained Markov Decision Process (CMDP) with an unknown transition probability matrix, where the safety requirements are modeled as constraints on expected cumulative costs. We propose two model-based constrained reinforcement learning (CRL) algorithms for learning a safe policy, namely, (i) GM-CRL algorithm, where the algorithm has access to
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HomChaudhuri, Baisravan, and Carrie M. Hall. "Model Predictive Control Based Energy Management of Power-Split Hybrid Electric Vehicles in Presence of Uncertainty." In ASME 2018 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/dscc2018-8961.

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A model predictive control (MPC) based robust energy management strategy for power-split hybrid electric vehicles (HEVs) is proposed in this paper which can guarantee charge sustaining constraint satisfaction in presence of uncertainty in future torque demand and vehicle velocity. The proposed method utilizes robust backward reachability analysis to pre-compute a robust set from where control solutions exist that ensure charge sustaining constraint satisfaction despite the uncertainty. This set is then used as the terminal set constraint of the MPC problem followed by tightening of state const
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Reports on the topic "Constraints satisfaction problem"

1

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

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Sadeh, Norman M., and Mark S. Fox. Variable and Value Ordering Heuristics for the Job Shop Scheduling Constraint Satisfaction Problem. Defense Technical Information Center, 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, 2000. http://dx.doi.org/10.15760/etd.11.

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