Relevant bibliographies by topics / Constraint-based solver

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

Academic literature on the topic 'Constraint-based solver'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Constraint-based solver"

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Blynov, I. O. "PROPAGATION-BASED CONSTRAINT SOLVER IN IMS." Information Technologies in Education, no. 11 (April 12, 2012): 78–82. http://dx.doi.org/10.14308/ite000304.

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Chun-Chen, Liu. "Slicing Floorplan Using BDD-based Constraint Solver." Journal of Applied Sciences 6, no. 2 (2006): 297–302. http://dx.doi.org/10.3923/jas.2006.297.302.

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Joan-Arinyo, R., and A. Soto. "A correct rule-based geometric constraint solver." Computers & Graphics 21, no. 5 (1997): 599–609. http://dx.doi.org/10.1016/s0097-8493(97)00038-1.

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Gange, G., P. J. Stuckey, and V. Lagoon. "Fast Set Bounds Propagation Using a BDD-SAT Hybrid." Journal of Artificial Intelligence Research 38 (June 25, 2010): 307–38. http://dx.doi.org/10.1613/jair.3014.

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Binary Decision Diagram (BDD) based set bounds propagation is a powerful approach to solving set-constraint satisfaction problems. However, prior BDD based techniques in- cur the significant overhead of constructing and manipulating graphs during search. We present a set-constraint solver which combines BDD-based set-bounds propagators with the learning abilities of a modern SAT solver. Together with a number of improvements beyond the basic algorithm, this solver is highly competitive with existing propagation based set constraint solvers.
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Ghanbari Ghooshchi, Nina, Majid Namazi, M. A. Hakim Newton, and Abdul Sattar. "Encoding Domain Transitions for Constraint-Based Planning." Journal of Artificial Intelligence Research 58 (April 28, 2017): 905–66. http://dx.doi.org/10.1613/jair.5378.

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We describe a constraint-based automated planner named Transition Constraints for Parallel Planning (TCPP). TCPP constructs its constraint model from a redefined version of the domain transition graphs (DTG) of a given planning problem. TCPP encodes state transitions in the redefined DTGs by using table constraints with cells containing don't cares or wild cards. TCPP uses Minion the constraint solver to solve the constraint model and returns a parallel plan. We empirically compare TCPP with the other state-of-the-art constraint-based parallel planner PaP2. PaP2 encodes action successions in the finite state automata (FSA) as table constraints with cells containing sets of values. PaP2 uses SICStus Prolog as its constraint solver. We also improve PaP2 by using don’t cares and mutex constraints. Our experiments on a number of standard classical planning benchmark domains demonstrate TCPP's efficiency over the original PaP2 running on SICStus Prolog and our reconstructed and enhanced versions of PaP2 running on Minion.
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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 infinite.
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DUCK, GREGORY J. "SMCHR: Satisfiability modulo constraint handling rules." Theory and Practice of Logic Programming 12, no. 4-5 (2012): 601–18. http://dx.doi.org/10.1017/s1471068412000208.

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AbstractConstraint Handling Rules (CHRs) are a high-level rule-based programming language for specification and implementation of constraint solvers. CHR manipulates a global store representing a flat conjunction of constraints. By default, CHR does not support goals with a more complex propositional structure including disjunction, negation, etc., or CHR relies on the host system to provide such features. In this paper we introduce Satisfiability Modulo Constraint Handling Rules (SMCHR): a tight integration of CHR with a modern Boolean Satisfiability (SAT) solver for quantifier-free formulae with an arbitrary propositional structure. SMCHR is essentially a Satisfiability Modulo Theories (SMT) solver where the theory T is implemented in CHR. The execution algorithm of SMCHR is based on lazy clause generation, where a new clause for the SAT solver is generated whenever a rule is applied. We shall also explore the practical aspects of building an SMCHR system, including extending a “built-in” constraint solver supporting equality with unification and justifications.
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WOLF, ARMIN. "Intelligent search strategies based on adaptive Constraint Handling Rules." Theory and Practice of Logic Programming 5, no. 4-5 (2005): 567–94. http://dx.doi.org/10.1017/s1471068405002383.

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The most advanced implementation of adaptive constraint processing with Constraint Handling Rules (CHR) allows the application of intelligent search strategies to solve Constraint Satisfaction Problems (CSP). This presentation compares an improved version of conflict-directed backjumping and two variants of dynamic backtracking with respect to chronological backtracking on some of the AIM instances which are a benchmark set of random 3-SAT problems. A CHR implementation of a Boolean constraint solver combined with these different search strategies in Java is thus being compared with a CHR implementation of the same Boolean constraint solver combined with chronological backtracking in SICStus Prolog. This comparison shows that the addition of “intelligence” to the search process may reduce the number of search steps dramatically. Furthermore, the runtime of their Java implementations is in most cases faster than the implementations of chronological backtracking. More specifically, conflict-directed backjumping is even faster than the SICStus Prolog implementation of chronological backtracking, although our Java implementation of CHR lacks the optimisations made in the SICStus Prolog system.
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LALLOUET, ARNAUD, and ANDREÏ LEGTCHENKO. "Partially defined constraints in constraint-based design." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 20, no. 4 (2006): 297–311. http://dx.doi.org/10.1017/s0890060406060227.

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In constraint-based design, components are modeled by variables describing their properties and subject to physical or mechanical constraints. However, some other constraints are difficult to represent, like comfort or user satisfaction. Partially defined constraints can be used to model the incomplete knowledge of a concept or a relation. Instead of only computing with the known part of the constraint, we propose to complete its definition by using machine-learning techniques. Because constraints are actively used during solving for pruning domains, building a classifier for instances is not enough: we need a solver able to reduce variable domains. Our technique is composed of two steps: first we learn a classifier for the constraint's projections and then we transform the classifier into a propagator. We show that our technique not only has good learning performances but also yields a very efficient solver for the learned constraint.
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TALBOT, PIERRE, ÉRIC MONFROY, and CHARLOTTE TRUCHET. "Modular Constraint Solver Cooperation via Abstract Interpretation." Theory and Practice of Logic Programming 20, no. 6 (2020): 848–63. http://dx.doi.org/10.1017/s1471068420000162.

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AbstractCooperation among constraint solvers is difficult because different solving paradigms have different theoretical foundations. Recent works have shown that abstract interpretation can provide a unifying theory for various constraint solvers. In particular, it relies on abstract domains which capture constraint languages as ordered structures. The key insight of this paper is viewing cooperation schemes as abstract domains combinations. We propose a modular framework in which solvers and cooperation schemes can be seamlessly added and combined. This differs from existing approaches such as SMT where the cooperation scheme is usually fixed (e.g., Nelson-Oppen). We contribute to two new cooperation schemes: (i) interval propagators completion that allows abstract domains to exchange bound constraints, and (ii) delayed product which exchanges over-approximations of constraints between two abstract domains. Moreover, the delayed product is based on delayed goal of logic programming, and it shows that abstract domains can also capture control aspects of constraint solving. Finally, to achieve modularity, we propose the shared product to combine abstract domains and cooperation schemes. Our approach has been fully implemented, and we provide various examples on the flexible job shop scheduling problem.
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Dissertations / Theses on the topic "Constraint-based solver"

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Subramanian, Prasad. "A Field Programmable Gate Array Based Finite-Domain Constraint Solver." DigitalCommons@USU, 2008. https://digitalcommons.usu.edu/etd/99.

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Constraint satisfaction and optimization techniques are commonly employed in scheduling problems, industrial manufacturing, and automation processes. Constraint Satisfaction Problem (CSP) also finds use in the design, synthesis, and optimization of embedded systems. In recent years online constraint solving techniques have been employed in embedded systems for dynamic system adaptation. In embedded systems, online constraint solving techniques are primarily used as on-board control software. Using CSP techniques for scheduling algorithms provides intelligent scheduling. This thesis discusses the architecture of an embedded, parallel finite-domain constraint solver for performing online constraint satisfaction. By modeling the scheduling problem as a CSP problem, the embedded system becomes adaptable to dynamic changes in the environment. The features of this solver are that it is implemented in a platform with multiple soft-core processors with distributed memory architecture. A tool is also developed that automates the partitioning of the given application and configures the underlying framework.
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Subramanian, Gayatri. "Automating Component-Based System Assembly." Thesis, Georgia Institute of Technology, 2006. http://hdl.handle.net/1853/11508.

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Owing to advancements in component re-use technology, component-based software development (CBSD) has come a long way in developing complex commercial software systems while reducing software development time and cost. However, assembling distributed resource-constrained and safety-critical systems using current assembly techniques is a challenge. Within complex systems when there are numerous ways to assemble the components unless the software architecture clearly defines how the components should be composed, determining the correct assembly that satisfies the system assembly constraints is difficult. Component technologies like CORBA and .NET do a very good job of integrating components, but they do not automate component assembly; it is the system developer's responsibility to ensure thatthe components are assembled correctly. In this thesis, we first define a component-based system assembly (CBSA) technique called "Constrained Component Assembly Technique" (CCAT), which is useful when the system has complex assembly constraints and the system architecture specifies component composition as assembly constraints. The technique poses the question: Does there exist a way of assembling the components that satisfies all the connection, performance, reliability, and safety constraints of the system, while optimizing the objective constraint? To implement CCAT, we present a powerful framework called "CoBaSA". The CoBaSA framework includes an expressive language for declaratively describing component functional and extra-functional properties, component interfaces, system-level and component-level connection, performance, reliability, safety, and optimization constraints. To perform CBSA, we first write a program (in the CoBaSA language) describing the CBSA specifications and constraints, and then an interpreter translates the CBSA program into a satisfiability and optimization problem. Solving the generated satisfiability and optimization problem is equivalent to answering the question posed by CCAT. If a satisfiable solution is found, we deduce that the system can be assembled without violating any constraints. Since CCAT and CoBaSA provide a mechanism for assembling systems that have complex assembly constraints, they can be utilized in several industries like the avionics industry. We demonstrate the merits of CoBaSA by assembling an actual avionic system that could be used on-board a Boeing aircraft. The empirical evaluation shows that our approach is promising and can scale to handle complex industrial problems.
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Bracci, Elia. "Studio di un applicativo per la pianificazione dell'orario universitario." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23163/.

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La tesi presenta lo studio effettuato per valutare la possibilità di integrare gli algoritmi di ottimizzazione del sistema di pianificazione della didattica dell'applicativo open source UniTime nell’applicativo UP2.0 sviluppato da Cineca. Il documento è strutturato come segue: il primo capitolo contiene un'analisi generale sul sistema universitario italiano e sul sistema universitario americano, paese di riferimento di UniTime; segue un capitolo di descrizione del prodotto di Cineca UP2.0, per definirne le funzionalità e le possibili estensioni. La tesi continua con un capitolo dedicato al sistema UniTime, la sua struttura, le sue funzionalità e un capitolo interamente dedicato all'algoritmo utilizzato per la risoluzione dei problemi di pianificazione degli orari. Infine, si conclude con i capitoli sull'utilizzo e l'integrazione di UniTime con il modello dati del prodotto di Cineca, i risultati ottenuti e le conclusioni.
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Probst, Christian W. [Verfasser]. "A demand-driven solver for constraint-based control flow analysis / von Christian W. Probst." 2004. http://d-nb.info/972321683/34.

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Books on the topic "Constraint-based solver"

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Malouf, Robert. Defaults and lexical prototypes. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198712329.003.0009.

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Inheritance plays several distinct but crucial roles in the representation of linguistic information in Head-Driven Phrase Structure Grammar and other constraint-based frameworks. The primary use of inheritance is in the definition of the type signature, a specification of what counts as a well-formed linguistic object. In more recent developments of the theory, inheritance is also used to express substantive linguistic generalizations. This latter use is quite different from the original problem that inheritance was introduced into HPSG to solve, and there are other knowledge representation devices that might be more appropriate. In particular, delegation can also be used to express linguistic generalizations. The use of delegation can simplify HPSG analyses of some phenomena and can also clarify some of the issues that arise in the use default unification.
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Book chapters on the topic "Constraint-based solver"

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Barth, Peter. "A Logic Cut Based Constraint Solver." In Operations Research/Computer Science Interfaces Series. Springer US, 1996. http://dx.doi.org/10.1007/978-1-4613-1315-1_4.

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Zoeteweij, Peter, and Farhad Arbab. "A Component-Based Parallel Constraint Solver." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24634-3_23.

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Pelleau, Marie, Antoine Miné, Charlotte Truchet, and Frédéric Benhamou. "A Constraint Solver Based on Abstract Domains." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-35873-9_26.

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Bouhineau, Denis. "Solving geometrical constraint systems using CLP based on linear constraint solver." In Artificial Intelligence and Symbolic Mathematical Computation. Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/3-540-61732-9_63.

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Kawamura, Toshio, Hayato Ohwada, and Fumio Mizoguchi. "CS-Prolog: A generalized unification based constraint solver." In Logic Programming '87. Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/3-540-19426-6_2.

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Mauss, Jakob, Frank Seelisch, and Mugur Tatar. "A Relational Constraint Solver for Model-Based Engineering." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-46135-3_48.

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Albajes, Lluis Solano, and Pere Brunet Crosa. "A Geometric Relaxation Solver for Constraint-Based Models." In CAD Tools and Algorithms for Product Design. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-662-04123-9_17.

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Alviano, Mario, Carmine Dodaro, Wolfgang Faber, Nicola Leone, and Francesco Ricca. "WASP: A Native ASP Solver Based on Constraint Learning." In Logic Programming and Nonmonotonic Reasoning. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40564-8_6.

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Rigotti, Christophe, Ieva Mitašiūnaitė, Jérémy Besson, Laurène Meyniel, Jean-François Boulicaut, and Olivier Gandrillon. "Using a Solver Over the String Pattern Domain to Analyze Gene Promoter Sequences." In Inductive Databases and Constraint-Based Data Mining. Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-7738-0_17.

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Kircanski, Aleksandar. "Analysis of Boomerang Differential Trails via a SAT-Based Constraint Solver URSA." In Applied Cryptography and Network Security. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-28166-7_16.

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Conference papers on the topic "Constraint-based solver"

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Lagniez, Jean-Marie, Emmanuel Lonca, and Jean-Guy Mailly. "CoQuiAAS: A Constraint-Based Quick Abstract Argumentation Solver." In 2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2015. http://dx.doi.org/10.1109/ictai.2015.134.

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Kefan, Ma, Xiao Liquan, Zhang Jianmin, and Li Tiejun. "An FPGA SAT solver based on enhanced constraint." In 2017 International Conference on FPGA Reconfiguration for General-Purpose Computing (FPGA4GPC). IEEE, 2017. http://dx.doi.org/10.1109/fpga4gpc.2017.8008962.

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Akgün, Özgür, Saad Attieh, Ian P. Gent, et al. "A Framework for Constraint Based Local Search using Essence." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/173.

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Structured Neighbourhood Search (SNS) is a framework for constraint-based local search for problems expressed in the Essence abstract constraint specification language. The local search explores a structured neighbourhood, where each state in the neighbourhood preserves a high level structural feature of the problem. SNS derives highly structured problem-specific neighbourhoods automatically and directly from the features of the Essence specification of the problem. Hence, neighbourhoods can represent important structural features of the problem, such as partitions of sets, even if that structure is obscured in the low-level input format required by a constraint solver. SNS expresses each neighbourhood as a constrained optimisation problem, which is solved with a constraint solver. We have implemented SNS, together with automatic generation of neighbourhoods for high level structures, and report high quality results for several optimisation problems.
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Sawada, Hiroyuki, and Xiu-Tian Yan. "Preliminary Design Support System Based on a Generic Under-Constraint Solving Technique." In ASME 2000 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2000. http://dx.doi.org/10.1115/detc2000/dac-14525.

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Abstract Engineering design is an intensive decision making process. A designer with an informative and insightful decision making support can usually produce high quality product design solutions with less or no rework. However, with current support designers very often face challenge or even difficulties as more and more design parameters come into design decision making process when a design progresses. This paper proposes a novel approach to providing designers with such a decision support by using under-constraint design problem solver. It is argued that design requirements represented in the form of Product Design Specifications (PDSs) can be converted into a set of related constraint expressions. These PDS constraint sets, which are usually incomplete, i.e., under-constrained, can then be solved by the solver to provide a designer with guided solutions for each design parameter, thus support a designer to make an informative and insightful design decision. A case study is finally presented in the paper to demonstrate how this approach is used to solve a real engineering design problem — a robotic finger system design.
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Bayless, Sam, Nodir Kodirov, Ivan Beschastnikh, Holger H. Hoos, and Alan J. Hu. "Scalable Constraint-based Virtual Data Center Allocation." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/77.

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Constraint-based techniques can solve challenging problems arising from highly diverse applications. This paper considers the problem of virtual data center (VDC) allocation, an important, emerging challenge for modern data center operators. To solve this problem, we introduce NETSOLVER, which is based on the general-purpose constraint solver MONOSAT. NETSOLVER represents a major improvement over existing approaches: it is sound, complete, and scalable, providing support for end-to-end, multi-path bandwidth guarantees across all the layers of hosting infrastructure, from servers to top-of-rack switches to aggregation switches to access routers. NETSOLVER scales to realistic data center sizes and VDC topologies, typically requiring just seconds to allocate VDCs of 5–15 virtual machines to physical data centers with 1000+ servers, maintaining this efficiency even when the data center is nearly saturated. In many cases, NETSOLVER can allocate 150%−300% as many total VDCs to the same physical data center as previous methods. Essential to our solution efficiency is our formulation of VDC allocation using monotonic theories, illustrating the practical value of the recently proposed SAT modulo monotonic theories approach.
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Xiao, Hao, Zheng Wang, Gegang Pu, and Bin Gu. "Constraint Checking for XML-Based Language Specification by SAT Solver." In 2010 Fourth International Conference on Secure Software Integration and Reliability Improvement Companion (SSIRI-C). IEEE, 2010. http://dx.doi.org/10.1109/ssiri-c.2010.15.

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Khanna, Dhriti, Rahul Purandare, and Subodh Sharma. "Verifying and Testing Concurrent Programs using Constraint Solver based Approaches." In 2020 IEEE International Conference on Software Maintenance and Evolution (ICSME). IEEE, 2020. http://dx.doi.org/10.1109/icsme46990.2020.00105.

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Talbot, Pierre. "Search Strategies as Synchronous Processes (Extended Abstract)." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/766.

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Solving constraint satisfaction problems (CSP) efficiently depends on the solver configuration and the search strategy. However, it is difficult to customize the constraint solvers because they are not modular enough, and it is hard to create new search strategies by composition. To solve these problems, we propose spacetime programming, a paradigm based on lattices and synchronous process calculi that views search strategies as processes working collaboratively towards the resolution of a CSP. We implement the compiler of the language and use it to replace the search module of Choco, a state of the art constraint solver, with an efficient spacetime program that offers better modularity and compositionality of search strategies.
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Ali, Shaukat, Muhammad Zohaib Iqbal, Andrea Arcuri, and Lionel Briand. "A Search-Based OCL Constraint Solver for Model-Based Test Data Generation." In 2011 11th International Conference on Quality Software (QSIC). IEEE, 2011. http://dx.doi.org/10.1109/qsic.2011.17.

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Edvardsson, Jon, and Mariam Kamkar. "Analysis of the constraint solver in UNA based test data generation." In the 8th European software engineering conference held jointly with 9th ACM SIGSOFT international symposium. ACM Press, 2001. http://dx.doi.org/10.1145/503209.503242.

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