Artykuły w czasopismach: „Constraint Satisfaction Programming”

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Van Hentenryck, Pascal, Helmut Simonis i Mehmet Dincbas. "Constraint satisfaction using constraint logic programming". Artificial Intelligence 58, nr 1-3 (grudzień 1992): 113–59. http://dx.doi.org/10.1016/0004-3702(92)90006-j.

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APT, KRZYSZTOF R., i ERIC MONFROY. "Constraint programming viewed as rule-based programming". Theory and Practice of Logic Programming 1, nr 6 (listopad 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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Booth, Kyle E. C., Bryan O'Gorman, Jeffrey Marshall, Stuart Hadfield i Eleanor Rieffel. "Quantum-accelerated constraint programming". Quantum 5 (28.09.2021): 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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Rossi, Francesca, Kristen Brent Venable i Toby Walsh. "Preferences in Constraint Satisfaction and Optimization". AI Magazine 29, nr 4 (28.12.2008): 58. http://dx.doi.org/10.1609/aimag.v29i4.2202.

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We review constraint-based approaches to handle preferences. We start by defining the main notions of constraint programming, then give various concepts of soft constraints and show how they can be used to model quantitative preferences. We then consider how soft constraints can be adapted to handle other forms of preferences, such as bipolar, qualitative, and temporal preferences. Finally, we describe how AI techniques such as abstraction, explanation generation, machine learning, and preference elicitation, can be useful in modelling and solving soft constraints.

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Rossi, Francesca. "Constraint satisfaction problems in logic programming". ACM SIGART Bulletin, nr 106 (październik 1988): 24–28. http://dx.doi.org/10.1145/54350.54352.

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LEUNG, HO-FUNG, i KEITH L. CLARK. "Constraint Satisfaction in Distributed Concurrent Logic Programming". Journal of Symbolic Computation 21, nr 4-6 (kwiecień 1996): 699–714. http://dx.doi.org/10.1006/jsco.1996.0037.

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Alpuente, María, Moreno Falaschi i Giorgio Levi. "Incremental constraint satisfaction for equational logic programming". Theoretical Computer Science 142, nr 1 (maj 1995): 27–57. http://dx.doi.org/10.1016/0304-3975(94)00224-7.

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Ho Geun Lee, Ronald M. Lee i Gang Yu. "Constraint logic programming for qualitative and quantitative constraint satisfaction problems". Decision Support Systems 16, nr 1 (styczeń 1996): 67–83. http://dx.doi.org/10.1016/0167-9236(94)00057-3.

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ZHANG, YUANLIN, i ROLAND H. C. YAP. "Solving functional constraints by variable substitution". Theory and Practice of Logic Programming 11, nr 2-3 (4.02.2011): 297–322. http://dx.doi.org/10.1017/s1471068410000591.

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AbstractFunctional constraints and bi-functional constraints are an important constraint class in Constraint Programming (CP) systems, in particular for Constraint Logic Programming (CLP) systems. CP systems with finite domain constraints usually employ Constraint Satisfaction Problem(s)-based solvers which use local consistency, for example, arc consistency. We introduce a new approach which is based instead on variable substitution. We obtain efficient algorithms for reducing systems involving functional and bi-functional constraints together with other nonfunctional constraints. It also solves globally any CSP where there exists a variable such that any other variable is reachable from it through a sequence of functional constraints. Our experiments on random problems show that variable elimination can significantly improve the efficiency of solving problems with functional constraints.

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Ligęza, Antoni. "Models and Tools for Improving Efficiency in Constraint Logic Programming". Decision Making in Manufacturing and Services 5, nr 1 (3.10.2011): 69–78. http://dx.doi.org/10.7494/dmms.2011.5.1.69.

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Constraint Satisfaction Problems typically exhibit strong combinatorial explosion. In this paper we present some models and techniques aimed at improving efficiency in Constraint Logic Programming. A hypergraph model of constraints is presented and an outline of strategy planning approach focused on entropy minimization is put forward. An example cryptoaritmetic problem is explored in order to explain the proposed approach.

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Pesant, G., C. Quimper i A. Zanarini. "Counting-Based Search: Branching Heuristics for Constraint Satisfaction Problems". Journal of Artificial Intelligence Research 43 (26.02.2012): 173–210. http://dx.doi.org/10.1613/jair.3463.

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Designing a search heuristic for constraint programming that is reliable across problem domains has been an important research topic in recent years. This paper concentrates on one family of candidates: counting-based search. Such heuristics seek to make branching decisions that preserve most of the solutions by determining what proportion of solutions to each individual constraint agree with that decision. Whereas most generic search heuristics in constraint programming rely on local information at the level of the individual variable, our search heuristics are based on more global information at the constraint level. We design several algorithms that are used to count the number of solutions to specific families of constraints and propose some search heuristics exploiting such information. The experimental part of the paper considers eight problem domains ranging from well-established benchmark puzzles to rostering and sport scheduling. An initial empirical analysis identifies heuristic maxSD as a robust candidate among our proposals.eWe then evaluate the latter against the state of the art, including the latest generic search heuristics, restarts, and discrepancy-based tree traversals. Experimental results show that counting-based search generally outperforms other generic heuristics.

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Zuenko, Alexander A., Olga V. Fridman i 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, nr 5-2021 (27.12.2021): 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 the issue of reducing the number and simplifying the constraints of the constraint satisfaction problem at the stage of its formalization. Within the framework of the approach, we have created: a) a method for estimating the optimal value of the objective function by hierarchical clustering of multisets, taking into account a priori constraints of the subject domain, and b) a method for generating additional constraints on the desired solution in the form of “smart tables”, based on the obtained estimate. The approach allows us to find the best partition in the problems of the class under consideration, which are characterized by a high dimension.

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Arangú, Marlene, i Miguel Salido. "A fine-grained arc-consistency algorithm for non-normalized constraint satisfaction problems". International Journal of Applied Mathematics and Computer Science 21, nr 4 (1.12.2011): 733–44. http://dx.doi.org/10.2478/v10006-011-0058-2.

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A fine-grained arc-consistency algorithm for non-normalized constraint satisfaction problems Constraint programming is a powerful software technology for solving numerous real-life problems. Many of these problems can be modeled as Constraint Satisfaction Problems (CSPs) and solved using constraint programming techniques. However, solving a CSP is NP-complete so filtering techniques to reduce the search space are still necessary. Arc-consistency algorithms are widely used to prune the search space. The concept of arc-consistency is bidirectional, i.e., it must be ensured in both directions of the constraint (direct and inverse constraints). Two of the most well-known and frequently used arc-consistency algorithms for filtering CSPs are AC3 and AC4. These algorithms repeatedly carry out revisions and require support checks for identifying and deleting all unsupported values from the domains. Nevertheless, many revisions are ineffective, i.e., they cannot delete any value and consume a lot of checks and time. In this paper, we present AC4-OP, an optimized version of AC4 that manages the binary and non-normalized constraints in only one direction, storing the inverse founded supports for their later evaluation. Thus, it reduces the propagation phase avoiding unnecessary or ineffective checking. The use of AC4-OP reduces the number of constraint checks by 50% while pruning the same search space as AC4. The evaluation section shows the improvement of AC4-OP over AC4, AC6 and AC7 in random and non-normalized instances.

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DRESCHER, CHRISTIAN, i TOBY WALSH. "A translational approach to constraint answer set solving". Theory and Practice of Logic Programming 10, nr 4-6 (lipiec 2010): 465–80. http://dx.doi.org/10.1017/s1471068410000220.

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AbstractWe present a new approach to enhancing Answer Set Programming (ASP) with Constraint Processing techniques which allows for solving interesting Constraint Satisfaction Problems in ASP. We show how constraints on finite domains can be decomposed into logic programs such that unit-propagation achieves arc, bound or range consistency. Experiments with our encodings demonstrate their computational impact.

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LEE, J. H. M., i V. W. L. TAM. "A FRAMEWORK FOR INTEGRATING ARTIFICIAL NEURAL NETWORKS AND LOGIC PROGRAMMING". International Journal on Artificial Intelligence Tools 04, nr 01n02 (czerwiec 1995): 3–32. http://dx.doi.org/10.1142/s0218213095000024.

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Many real-life problems belong to the class of constraint satisfaction problems (CSP’s), which are NP-complete, and some NP-hard, in general. When the problem size grows, it becomes difficult to program solutions and to execute the solution in a timely manner. In this paper, we present a general framework for integrating artificial neural networks and logic programming so as to provide an efficient and yet expressive programming environment for solving CSP’s. To realize this framework, we propose PROCLANN, a novel constraint logic programming language. The PROCLANN language retains the simple and elegant declarative semantics of constraint logic programming. Operationally, PROCLANN uses the standard goal reduction strategy in the frontend to generate constraints, and an efficient backend constraint-solver based on artificial neural networks. Its operational semantics is probabilistic in nature. We show that PROCLANN is sound and weakly complete. A novelty of PROCLANN is that while it is a committed-choice language, PROCLANN supports non-determinism, allowing the generation of multiple answers to a query. An initial prototype implementation of PROCLANN is constructed and demonstrates empirically that PROCLANN out-performs the state of art in constraint logic programming implementation on certain hard instances of CSP’s.

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Sitek, Paweł, i Jarosław Wikarek. "A Hybrid Programming Framework for Modeling and Solving Constraint Satisfaction and Optimization Problems". Scientific Programming 2016 (2016): 1–13. http://dx.doi.org/10.1155/2016/5102616.

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This paper proposes a hybrid programming framework for modeling and solving of constraint satisfaction problems (CSPs) and constraint optimization problems (COPs). Two paradigms, CLP (constraint logic programming) and MP (mathematical programming), are integrated in the framework. The integration is supplemented with the original method of problem transformation, used in the framework as a presolving method. The transformation substantially reduces the feasible solution space. The framework automatically generates CSP and COP models based on current values of data instances, questions asked by a user, and set of predicates and facts of the problem being modeled, which altogether constitute a knowledge database for the given problem. This dynamic generation of dedicated models, based on the knowledge base, together with the parameters changing externally, for example, the user’s questions, is the implementation of the autonomous search concept. The models are solved using the internal or external solvers integrated with the framework. The architecture of the framework as well as its implementation outline is also included in the paper. The effectiveness of the framework regarding the modeling and solution search is assessed through the illustrative examples relating to scheduling problems with additional constrained resources.

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Soto, Ricardo, Broderick Crawford, Wenceslao Palma, Eric Monfroy, Rodrigo Olivares, Carlos Castro i Fernando Paredes. "Top-kBased Adaptive Enumeration in Constraint Programming". Mathematical Problems in Engineering 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/580785.

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Constraint programming effectively solves constraint satisfaction and optimization problems by basically building, pruning, and exploring a search tree of potential solutions. In this context, a main component is the enumeration strategy, which is responsible for selecting the order in which variables and values are selected to build a possible solution. This process is known to be quite important; indeed a correct selection can reach a solution without failed explorations. However, it is well known that selecting the right strategy is quite challenging as their performance is notably hard to predict. During the last years, adaptive enumeration appeared as a proper solution to this problem. Adaptive enumeration allows the solving algorithm being able to autonomously modifying its strategies in solving time depending on performance information. In this way, the most suitable order for variables and values is employed along the search. In this paper, we present a new and more lightweight approach for performing adaptive enumeration. We incorporate a powerful classification technique named Top-kin order to adaptively select strategies along the resolution. We report results on a set of well-known benchmarks where the proposed approach noticeably competes with classical and modern adaptive enumeration methods for constraint satisfaction.

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DIACONESCU, RĂZVAN. "Category-based constraint logic". Mathematical Structures in Computer Science 10, nr 3 (czerwiec 2000): 373–407. http://dx.doi.org/10.1017/s0960129599002960.

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The research reported in this paper exploits the view of constraint programming as computation in a logical system, namely constraint logic. The basic ingredients of constraint logic are: constraint models for the semantics (they form a comma-category over a fixed model of ‘built-ins’); generalized polynomials in the role of basic syntactic ingredient; and a constraint satisfaction relation between semantics and syntax. Category-based constraint logic means the development of the logic is abstract categorical rather than concrete set theoretical.We show that (category-based) constraint logic is an institution, and we internalize the study of constraint logic to the abstract framework of category-based equational logic, thus opening the door for considering constraint logic programming over non-standard structures (such as CPO's, topologies, graphs, categories, etc.). By embedding category-based constraint logic into category-based equational logic, we integrate the constraint logic programming paradigm into (category-based) equational logic programming. Results include completeness of constraint logic deduction, a novel Herbrand theorem for constraint logic programming characterizing Herbrand models as initial models in constraint logic, and logical foundations for the modular combination of constraint solvers based on amalgamated sums of Herbrand models in the constraint logic institution.

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Khorsandi, Armita, Bing-Yuan Cao i Hadi Nasseri. "A New Method to Optimize the Satisfaction Level of the Decision Maker in Fuzzy Geometric Programming Problems". Mathematics 7, nr 5 (23.05.2019): 464. http://dx.doi.org/10.3390/math7050464.

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Geometric programming problems are well-known in mathematical modeling. They are broadly used in diverse practical fields that are contemplated through an appropriate methodology. In this paper, a multi-parametric vector α is proposed for approaching the highest decision maker satisfaction. Hitherto, the simple parameter α , which has a scalar role, has been considered in the problem. The parameter α is a vector whose range is within the region of the satisfaction area. Conventionally, it is assumed that the decision maker is sure about the parameters, but, in reality, it is mostly hesitant about them, so the parameters are presented in fuzzy numbers. In this method, the decision maker can attain different satisfaction levels in each constraint, and even full satisfaction can be reached in some constraints. The goal is to find the highest satisfaction degree to maintain an optimal solution. Moreover, the objective function is turned into a constraint, i.e., one more dimension is added to n-dimensional multi-parametric α . Thus, the fuzzy geometric programming problem under this multi-parametric vector α ∈ ( 0 , 1 ] n + 1 gives a maximum satisfaction level to the decision maker. A numerical example is presented to illustrate the proposed method and the superiority of this multi-parametric α over the simple one.

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Liu, Cheng, i Kun Kun Gu. "Location Problems of the Distribution Center under Uncertain Environment". Advanced Materials Research 171-172 (grudzień 2010): 617–21. http://dx.doi.org/10.4028/www.scientific.net/amr.171-172.617.

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Based on conventional location problems of distribution centers, the location problem of multiple distribution centers was considered, in which both the plant capacity and clients’ demands were fuzzy parameters. A mathematical model was built with fuzzy constraints and converted to an interval programming model based on cut set- by means of introducing the concept of cut set. Then, the interval constraint was converted to its certain equivalence for solution through the satisfaction, thus the interval programming problem was further converted to a deterministic 0-1 mixed integer programming problem for solution.

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DE CAT, BROES, i MAURICE BRUYNOOGHE. "Detection and exploitation of functional dependencies for model generation". Theory and Practice of Logic Programming 13, nr 4-5 (lipiec 2013): 471–85. http://dx.doi.org/10.1017/s147106841300029x.

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AbstractRecent work in Answer Set Programming has integrated ideas from Constraint Programming. This has led to a new field called ASP Modulo CSP (CASP), in which the ASP language is enriched with constraint atoms representing constraint satisfaction problems. These constraints have a more compact grounding and are handled by a new generation of search algorithms. However, the burden is on the modeler to exploit these new constructs in his declarative problem specifications. Here, we explore how to remove this burden by automatically generating constraint atoms. We do so in the context of FO(·)IDP, a knowledge representation language that extends first-order logic with, among others, inductive definitions, arithmetic and aggregates. We uncover functional dependencies in declarative problem specifications with a theorem prover and exploit them with a transformation that introduces functions. Experimental evaluation shows that we obtain more compact groundings and better search performance.

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STERGIOU, KOSTAS. "STRONG DOMAIN FILTERING CONSISTENCIES FOR NON-BINARY CONSTRAINT SATISFACTION PROBLEMS". International Journal on Artificial Intelligence Tools 17, nr 05 (październik 2008): 781–802. http://dx.doi.org/10.1142/s0218213008004163.

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Domain filtering local consistencies, such as inverse consistencies, that only delete values and do not add new constraints are particularly useful in Constraint Programming. Although many such consistencies for binary constraints have been proposed and evaluated, the situation with non-binary constraints is quite different. Only very recently have domain filtering consistencies stronger than GAC started to attract interest. Following this line of research, we define a number of strong domain filtering consistencies for non-binary constraints and theoretically compare their pruning power. We prove that three of these consistencies are equivalent to maxRPC in binary CSPs while another is equivalent to PIC. We also describe a generic algorithm for domain filtering consistencies in non-binary CSPs. We show how this algorithm can be instantiated to enforce some of the proposed consistencies and analyze the worst-case complexities of the resulting algorithms. Finally, we make a preliminary empirical study.

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Zein, S., P. Basso i S. Grihon. "A constraint satisfaction programming approach for computing manufacturable stacking sequences". Computers & Structures 136 (maj 2014): 56–63. http://dx.doi.org/10.1016/j.compstruc.2014.01.016.

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Hwang, Jun-Ha. "An Integration of Local Search and Constraint Programming for Solving Constraint Satisfaction Optimization Problems". Journal of the Korea Society of Computer and Information 15, nr 5 (31.05.2010): 39–47. http://dx.doi.org/10.9708/jksci.2010.15.5.039.

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Rutishauser, Ueli, Jean-Jacques Slotine i Rodney J. Douglas. "Solving Constraint-Satisfaction Problems with Distributed Neocortical-Like Neuronal Networks". Neural Computation 30, nr 5 (maj 2018): 1359–93. http://dx.doi.org/10.1162/neco_a_01074.

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Finding actions that satisfy the constraints imposed by both external inputs and internal representations is central to decision making. We demonstrate that some important classes of constraint satisfaction problems (CSPs) can be solved by networks composed of homogeneous cooperative-competitive modules that have connectivity similar to motifs observed in the superficial layers of neocortex. The winner-take-all modules are sparsely coupled by programming neurons that embed the constraints onto the otherwise homogeneous modular computational substrate. We show rules that embed any instance of the CSP's planar four-color graph coloring, maximum independent set, and sudoku on this substrate and provide mathematical proofs that guarantee these graph coloring problems will convergence to a solution. The network is composed of nonsaturating linear threshold neurons. Their lack of right saturation allows the overall network to explore the problem space driven through the unstable dynamics generated by recurrent excitation. The direction of exploration is steered by the constraint neurons. While many problems can be solved using only linear inhibitory constraints, network performance on hard problems benefits significantly when these negative constraints are implemented by nonlinear multiplicative inhibition. Overall, our results demonstrate the importance of instability rather than stability in network computation and offer insight into the computational role of dual inhibitory mechanisms in neural circuits.

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Berezhnov, D. E., i L. I. Minchenko. "Error Bound property in mathematical programming". Proceedings of the National Academy of Sciences of Belarus. Physics and Mathematics Series 55, nr 3 (7.10.2019): 309–18. http://dx.doi.org/10.29235/1561-2430-2019-55-3-309-318.

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This article is devoted to the Error Bound property (also named R-regularity) in mathematical programming problems. This property plays an important role in analyzing the convergence of numerical optimization algorithms, a topic covered by multiple publications, and at the same time it is a relatively generic constraint qualification that guarantees the satisfaction of the necessary Kuhn – Tucker optimality conditions in mathematical programming problems. In the article, new sufficient conditions for the error bound property are described, and it’s also shown that several known necessary conditions are insufficient. The sufficient conditions obtained can be used to prove the regularity of a large class of sets including sets that cannot be proven regular by other known constraints.

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Nair, Manjusha, Jinesh Manchan Kannimoola, Bharat Jayaraman, Bipin Nair i Shyam Diwakar. "Temporal constrained objects for modelling neuronal dynamics". PeerJ Computer Science 4 (23.07.2018): e159. http://dx.doi.org/10.7717/peerj-cs.159.

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Background Several new programming languages and technologies have emerged in the past few decades in order to ease the task of modelling complex systems. Modelling the dynamics of complex systems requires various levels of abstractions and reductive measures in representing the underlying behaviour. This also often requires making a trade-off between how realistic a model should be in order to address the scientific questions of interest and the computational tractability of the model. Methods In this paper, we propose a novel programming paradigm, called temporal constrained objects, which facilitates a principled approach to modelling complex dynamical systems. Temporal constrained objects are an extension of constrained objects with a focus on the analysis and prediction of the dynamic behaviour of a system. The structural aspects of a neuronal system are represented using objects, as in object-oriented languages, while the dynamic behaviour of neurons and synapses are modelled using declarative temporal constraints. Computation in this paradigm is a process of constraint satisfaction within a time-based simulation. Results We identified the feasibility and practicality in automatically mapping different kinds of neuron and synapse models to the constraints of temporal constrained objects. Simple neuronal networks were modelled by composing circuit components, implicitly satisfying the internal constraints of each component and interface constraints of the composition. Simulations show that temporal constrained objects provide significant conciseness in the formulation of these models. The underlying computational engine employed here automatically finds the solutions to the problems stated, reducing the code for modelling and simulation control. All examples reported in this paper have been programmed and successfully tested using the prototype language called TCOB. The code along with the programming environment are available at http://github.com/compneuro/TCOB_Neuron. Discussion Temporal constrained objects provide powerful capabilities for modelling the structural and dynamic aspects of neural systems. Capabilities of the constraint programming paradigm, such as declarative specification, the ability to express partial information and non-directionality, and capabilities of the object-oriented paradigm especially aggregation and inheritance, make this paradigm the right candidate for complex systems and computational modelling studies. With the advent of multi-core parallel computer architectures and techniques or parallel constraint-solving, the paradigm of temporal constrained objects lends itself to highly efficient execution which is necessary for modelling and simulation of large brain circuits.

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Malapert, Arnaud, Jean-Charles Régin i Mohamed Rezgui. "Embarrassingly Parallel Search in Constraint Programming". Journal of Artificial Intelligence Research 57 (20.11.2016): 421–64. http://dx.doi.org/10.1613/jair.5247.

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We introduce an Embarrassingly Parallel Search (EPS) method for solving constraint problems in parallel, and we show that this method matches or even outperforms state-of-the-art algorithms on a number of problems using various computing infrastructures. EPS is a simple method in which a master decomposes the problem into many disjoint subproblems which are then solved independently by workers. Our approach has three advantages: it is an efficient method; it involves almost no communication or synchronization between workers; and its implementation is made easy because the master and the workers rely on an underlying constraint solver, but does not require to modify it. This paper describes the method, and its applications to various constraint problems (satisfaction, enumeration, optimization). We show that our method can be adapted to different underlying solvers (Gecode, Choco2, OR-tools) on different computing infrastructures (multi-core, data centers, cloud computing). The experiments cover unsatisfiable, enumeration and optimization problems, but do not cover first solution search because it makes the results hard to analyze. The same variability can be observed for optimization problems, but at a lesser extent because the optimality proof is required. EPS offers good average performance, and matches or outperforms other available parallel implementations of Gecode as well as some solvers portfolios. Moreover, we perform an in-depth analysis of the various factors that make this approach efficient as well as the anomalies that can occur. Last, we show that the decomposition is a key component for efficiency and load balancing.

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Moummadi, Kamal, Rachida Abidar i Hicham Medromi. "Distributed Resource Allocation". International Journal of Mobile Computing and Multimedia Communications 4, nr 2 (kwiecień 2012): 49–62. http://dx.doi.org/10.4018/jmcmc.2012040104.

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The growth of technological capabilities of mobile devices, the evolution of wireless communication technologies, and the maturity of embedded systems contributed to expand the Machine to machine (M2M) concept. M2M refers to data communication between machines without human intervention. The objective of this paper is to present the grand schemes of a model to be used in an agricultural Decision support System. The authors start by explaining and justifying the need for a hybrid system that uses both Multi-Agent System (MAS) and Constraint Programming (CP) paradigms. Then, the authors propose an approach for Constraint Programming and Multi-Agent System mixing based on controller agent concept. The authors present concrete constraints and agents to be used in a distributed architecture based on the proposed approach for M2M services and agricultural decision support. The platform is built in Java using general interfaces of both MAS and Constraint Satisfaction Problem (CSP) platforms and the conception is made by agent UML (AUML).

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Williams, H. P., i Hong Yan. "Representations of the all_different Predicate of Constraint Satisfaction in Integer Programming". INFORMS Journal on Computing 13, nr 2 (maj 2001): 96–103. http://dx.doi.org/10.1287/ijoc.13.2.96.10515.

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Gür, Şeyda, Tamer Eren i Hacı Alakaş. "Surgical Operation Scheduling with Goal Programming and Constraint Programming: A Case Study". Mathematics 7, nr 3 (11.03.2019): 251. http://dx.doi.org/10.3390/math7030251.

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The achievement of health organizations’ goals is critically important for profitability. For this purpose, their resources, materials, and equipment should be efficiently used in the services they provide. A hospital has sensitive and expensive equipment, and the use of its equipment and resources needs to be balanced. The utilization of these resources should be considered in its operating rooms, as it shares both expense expenditure and revenue generation. This study’s primary aim is the effective and balanced use of equipment and resources in hospital operating rooms. In this context, datasets from a state hospital were used via the goal programming and constraint programming methods. According to the wishes of hospital managers, three scenarios were separately modeled in both methods. According to the obtained results, schedules were compared and analyzed according to the current situation. The hospital-planning approach was positively affected, and goals such as minimization cost, staff and patient satisfaction, prevention over time, and less use were achieved.

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Major, François, Guy Lapalme i Robert Cedergren. "Domain generating functions for solving constraint satisfaction problems". Journal of Functional Programming 1, nr 2 (kwiecień 1991): 213–27. http://dx.doi.org/10.1017/s0956796800020050.

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AbstractThis paper presents an application of functional programming: searching a domain for elements which satisfy certain constraints. We give a very general formulation of the problem and describe ‘generate and test’, ‘backtracking’ and ‘forward checking’ algorithms. We then introduce the concept of domain generating functions to capture a common optimization during the search process: using partial solutions to reduce the size of the search space. We compare the efficiency of the original algorithms and those using domain generating functions first with the ‘classical’ n-queens example, and then with a problem having larger domains to search which was inspired by an application in macromolecular structure determination. Using algorithms coded in Miranda, Haskell and Common Lisp, we show that a high order (lazy) functional language is a useful and efficient tool for prototyping search methods in large complex domains.

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Guimarans, D., R. Herrero, J. J. Ramos i S. Padrón. "Solving Vehicle Routing Problems Using Constraint Programming and Lagrangean Relaxation in a Metaheuristics Framework". International Journal of Information Systems and Supply Chain Management 4, nr 2 (kwiecień 2011): 61–81. http://dx.doi.org/10.4018/jisscm.2011040104.

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This paper presents a methodology based on the Variable Neighbourhood Search metaheuristic, applied to the Capacitated Vehicle Routing Problem. The presented approach uses Constraint Programming and Lagrangean Relaxation methods in order to improve algorithm’s efficiency. The complete problem is decomposed into two separated subproblems, to which the mentioned techniques are applied to obtain a complete solution. With this decomposition, the methodology provides a quick initial feasible solution which is rapidly improved by metaheuristics’ iterative process. Constraint Programming and Lagrangean Relaxation are also embedded within this structure to ensure constraints satisfaction and to reduce the calculation burden. By means of the proposed methodology, promising results have been obtained. Remarkable results presented in this paper include a new best-known solution for a rarely solved 200-customers test instance, as well as a better alternative solution for another benchmark problem.

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Zuenko, Alexander, Yurii Oleynik i Roman Makedonov. "A method for solving the open-pit mine production scheduling problem using the constraint programming paradigm". Journal of Physics: Conference Series 2060, nr 1 (1.10.2021): 012021. http://dx.doi.org/10.1088/1742-6596/2060/1/012021.

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Abstract A method is developed to solve the problem of open-pit mine production scheduling, which is based on Constraint Programming and is used in working out an optimal plan of mining operations, taking into account heterogeneous economical, technological and some others requirements imposed by the customer. An original model is proposed to represent the problem considered as a constraint satisfaction problem. As the customer may verify the requirements, the domain model should enable addition of the new constraints and/or eliminate the existing ones. With the domain model being developed, the method proposed makes it possible to reach accurate solutions at the expense of highly effective reduction algorithms for each type of constraints and at the expense of specialized heuristics for non-perspective alternatives reduction in a search tree. The approach proposed enables addition of different constraints, even those for which it is difficult to find an analytical expression.

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Finkel, Raphael, i Barry O'Sullivan. "Reasoning about conditional constraint specification problems and feature models". Artificial Intelligence for Engineering Design, Analysis and Manufacturing 25, nr 2 (20.04.2011): 163–74. http://dx.doi.org/10.1017/s0890060410000600.

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AbstractProduct configuration is a major industrial application domain for constraint satisfaction techniques. Conditional constraint satisfaction problems (CCSPs) and feature models (FMs) have been developed to represent configuration problems in a natural way. CCSPs are like constraint satisfaction problems (CSPs), but they also include potential variables, which might or might not exist in any given solution, as well as classical variables, which are required to take a value in every solution. CCSPs model, for example, options on a car, for which the style of sunroof (a variable) only makes sense if the car has a sunroof at all. FMs are directed acyclic graphs of features with constraints on edges. FMs model, for example, cell phone features, where utility functions are required, but the particular utility function “games” is optional, but requires Java support. We show that existing techniques from formal methods and answer set programming can be used to naturally model CCSPs and FMs. We demonstrate configurators in both approaches. An advantage of these approaches is that the model builder does not have to reformulate the CCSP or FM into a classic CSP, converting potential variables into classical variables by adding a “does not exist” value and modifying the problem constraints. Our configurators automatically reason about the model itself, enumerating all solutions and discovering several kinds of model flaws.

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Li, Chendong. "Towards Interesting Patterns of Hard CSPs with Functional Constraints". Proceedings of the AAAI Conference on Artificial Intelligence 24, nr 1 (5.07.2010): 1941–42. http://dx.doi.org/10.1609/aaai.v24i1.7782.

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The hardness of finite domain Constraint Satisfaction Problems (CSPs) is an important research topic in Constraint Programming (CP) community. In this paper, we study the association rule mining techniques together with rule deduction and propose a cascaded approach to extract interesting patterns of hard CSPs with functional constraints. Specifically, we generate random CSPs, collect controlling parameters and hardness characteristics by solving all the CSP instances. Afterwards, we apply association rule mining with rule deduction on the collected data set and further extract interesting patterns of the hardness of the randomly generated CSPs. As far as we know, this problem is investigated with data mining techniques for the first time.

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Zuenko, A. A. "Compact representation of constraints based on a new interpretation of the concept "tuple of a multi-place relation"". Ontology of designing 10, nr 4 (30.12.2020): 503–15. http://dx.doi.org/10.18287/2223-9537-2020-10-4-503-515.

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The article discusses various points of view on the concept of "tuple of a multi-place relation" used in mathematics and information technology. Special attention is paid to an evolution of the concept “tuple” within Constraint Programming technology where the emergence of new interpretations for the concept “tuple” is related with attempts to design more “compact” table representation of qualitative relations comparing with typical relational tables. This “compact” representation can serve as a basis for accelerating qualitative constraint satisfaction procedures. In studied prototype-works, such varieties of table constraints as compressed-tables and smart-tables were proposed. In so doing, the concept of compressed- and smart- tuple substantially differs from traditional concept of tuple of a multi-place relation. However, the known table constraints types are not well suited for modeling and processing all types of quality relations, for example, there are inconveniences when modeling production rules. The article proposes a new type of table constraints – smart-tables of D-type and equivalent transformations rules allowing the initial constraint satisfaction problem to be effectively reduced. The application of the smart-tables of D-type allows in some cases to substantially reduce computer memory consumption comparing with application of the known types of table constraints. In particular, smart-tables of D-types are well suited for modeling production rules, some types of logical expirations and some types of global constraints.

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Yvars, Pierre-Alain, i Laurent Zimmer. "System Sizing with a Model-Based Approach: Application to the Optimization of a Power Transmission System". Mathematical Problems in Engineering 2018 (26.07.2018): 1–14. http://dx.doi.org/10.1155/2018/6861429.

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We test the relevance of a model-based approach for sizing and optimizing complex systems. Classically a model-based approach is characterized by a clear partition between the problem description and the solving process. In the case of a design problem, we show that the sizing task could be systematically characterized and therefore could lead to a declarative model combining both system description and design requirements. Once translated into a constraint satisfaction problem, the resulting model can be solved with interval constraint programming methods and algorithms. Our first contribution to this approach is to precisely characterize the sizing task in design. The resulting terminology enables us to easily and systematically express the problem as a constraint satisfaction problem (CSP) which combines in the same model the system description and the design requirements. We have tested the approach on the optimal sizing problem of a power transmission system. Previous authors have described this scalable case study. They provide a mathematical formulation of the problem and solve it with an evolutionary algorithm. Starting from their description, we apply our methodology to model the problem as a CSP and then solve it with interval constraint programming algorithms. Our solutions are more adequate both in computational time and in optimization results than those published in the literature on the same problem. Moreover the declarative nature of constraint programming makes modifications or extensions easier than with evolutionary programming. The explanation of these results is our second contribution to the approach. However some important modelling issues remain to address in order to capture more and more complex system specifications. Further research is presented at the end of this paper.

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Hwang, Jun-Ha, i Sung-Young Kim. "Integer Programming-based Local Search Technique for Linear Constraint Satisfaction Optimization Problem". Journal of the Korea Society of Computer and Information 15, nr 9 (30.09.2010): 47–55. http://dx.doi.org/10.9708/jksci.2010.15.9.047.

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Relich, Marcin. "An evaluation of project completion with application of fuzzy set theory". Management 16, nr 1 (1.05.2012): 216–29. http://dx.doi.org/10.2478/v10286-012-0016-6.

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An evaluation of project completion with application of fuzzy set theoryThe project management contains such elements as management of time, cost, communications, procurement, quality, risk or scope of project. Each of these fields can be considered as a set of constraints, and then there is a possibility to verify their fulfillment in sense of an enterprise's constraints and its environment. These constraints determine a completion of project activities and its success or failure, finally. The paper aims to present a problem of project management in terms of fuzzy constraints satisfaction problem, and then the using of constraint programming techniques to the evaluation of project completion. A fuzzy constraints satisfaction problem enables a description of data in distinct, as well as imprecise form, in a unified framework. It seems especially important in case of unique activities of project, when their estimation is based on linguistic information from experts.

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VU, XUAN-HA, i BARRY O'SULLIVAN. "A UNIFYING FRAMEWORK FOR GENERALIZED CONSTRAINT ACQUISITION". International Journal on Artificial Intelligence Tools 17, nr 05 (październik 2008): 803–33. http://dx.doi.org/10.1142/s0218213008004175.

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When a practical problem can be modeled as a constraint satisfaction problem (CSP), which is a set of constraints that need to be satisfied, it can be solved using many constraint programming techniques. In many practical applications, while users can recognize examples of where a CSP should be satisfied or violated, they cannot articulate the specification of the CSP itself. In these situations, it can be helpful if the computer can take an active role in learning the CSP from examples of its solutions and non-solutions. This is called constraint acquisition. This paper introduces a framework for constraint acquisition in which one can uniformly define and formulate constraint acquisition problems of different types as optimization problems. The difference between constraint acquisition problems within the framework is not only in the type of constraints that need to be acquired but also in the learning objective. The generic framework can be instantiated to obtain specific formulations for acquiring classical, fuzzy, weighted or probabilistic constraints. The paper shows as an example how recent techniques for acquiring classical constraints can be directly obtained from the framework. Specifically, the formulation obtained from the framework to acquire classical CSPs with the minimum number of violated examples is equivalent to a simple pseudo-boolean optimization problem, thus being efficiently solvable by using many available optimization tools. The paper also reports empirical results on constraint acquisition methods to show the utility of the framework.

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Climent, L., R. J. Wallace, M. A. Salido i F. Barber. "Robustness and Stability in Constraint Programming under Dynamism and Uncertainty". Journal of Artificial Intelligence Research 49 (28.01.2014): 49–78. http://dx.doi.org/10.1613/jair.4126.

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Many real life problems that can be solved by constraint programming, come from uncertain and dynamic environments. Because of the dynamism, the original problem may change over time, and thus the solution found for the original problem may become invalid. For this reason, dealing with such problems has become an important issue in the fields of constraint programming. In some cases, there exist extant knowledge about the uncertain and dynamic environment. In other cases, this information is fragmentary or unknown. In this paper, we extend the concept of robustness and stability for Constraint Satisfaction Problems (CSPs) with ordered domains, where only limited assumptions need to be made as to possible changes. We present a search algorithm that searches for both robust and stable solutions for CSPs of this nature. It is well-known that meeting both criteria simultaneously is a desirable objective for constraint solving in uncertain and dynamic environments. We also present compelling evidence that our search algorithm outperforms other general-purpose algorithms for dynamic CSPs using random instances and benchmarks derived from real life problems.

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Boukadida, Haykel, Sid-Ahmed Berrani i Patrick Gros. "Automatically Creating Adaptive Video Summaries Using Constraint Satisfaction Programming: Application to Sport Content". IEEE Transactions on Circuits and Systems for Video Technology 27, nr 4 (kwiecień 2017): 920–34. http://dx.doi.org/10.1109/tcsvt.2015.2513678.

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Nakano, Takahiro, i Masahiro Nagamatu. "Lagrange programming neural network for solving constraint satisfaction problem and its extended problem". International Congress Series 1291 (czerwiec 2006): 193–96. http://dx.doi.org/10.1016/j.ics.2006.01.037.

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AMADINI, ROBERTO, MAURIZIO GABBRIELLI i JACOPO MAURO. "SUNNY: a Lazy Portfolio Approach for Constraint Solving". Theory and Practice of Logic Programming 14, nr 4-5 (lipiec 2014): 509–24. http://dx.doi.org/10.1017/s1471068414000179.

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AbstractWithin the context of constraint solving, a portfolio approach allows one to exploit the synergy between different solvers in order to create a globally better solver. In this paper we present SUNNY: a simple and flexible algorithm that takes advantage of a portfolio of constraint solvers in order to compute — without learning an explicit model — a schedule of them for solving a given Constraint Satisfaction Problem (CSP). Motivated by the performance reached by SUNNY vs. different simulations of other state of the art approaches, we developedsunny-csp, an effective portfolio solver that exploits the underlying SUNNY algorithm in order to solve a given CSP. Empirical tests conducted on exhaustive benchmarks of MiniZinc models show that the actual performance ofsunny-cspconforms to the predictions. This is encouraging both for improving the power of CSP portfolio solvers and for trying to export them to fields such as Answer Set Programming and Constraint Logic Programming.

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LAI, LIEN F., CHAO-CHIN WU, NIEN-LIN HSUEH, LIANG-TSUNG HUANG i SHIOW-FEN HWANG. "AN ARTIFICIAL INTELLIGENCE APPROACH TO COURSE TIMETABLING". International Journal on Artificial Intelligence Tools 17, nr 01 (luty 2008): 223–40. http://dx.doi.org/10.1142/s0218213008003868.

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Course Timetabling is a complex problem that cannot be dealt with by using only a few general principles. The various actors (the administrator, the chairman, the instructor and the student) have their own objectives, and these objectives usually conflict. The complexity of the relationships among time slots, classes, classrooms, and instructors makes it difficult to achieve a feasible solution. In this article, we propose an artificial intelligence approach that integrates expert systems and constraint programming to implement a course timetabling system. Expert systems are utilized to incorporate knowledge into the timetabling system and to provide a reasoning capability for knowledge deduction. Separating out the knowledge base, the facts, and the inference engine in expert systems provides greater flexibility in supporting changes. The constraint hierarchy and the constraint network are utilized to capture hard and soft constraints and to reason about constraints by using constraint satisfaction and relaxation techniques. In addition, object-oriented software engineering is applied to improve the development and maintenance of the course timetabling system. A course timetabling system in the Department of Computer Science and Information Engineering at the National Changhua University of Education (NCUE) is used as an illustrative example of the proposed approach.

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Gholamnejad, Javad, Ali Azimi, Reza Lotfian, Sara Kasmaeeyazdi i Francesco Tinti. "THE APPLICATION OF A STOCKPILE STOCHASTIC MODEL INTO LONG-TERM OPEN PIT MINE PRODUCTION SCHEDULING TO IMPROVE THE FEED GRADE FOR THE PROCESSING PLANT". Rudarsko-geološko-naftni zbornik 35, nr 4 (2020): 115–29. http://dx.doi.org/10.17794/rgn.2020.4.10.

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This paper presents a chance-constrained integer programming approach based on the linear method to solve the longterm open pit mine production scheduling problem. Specifically, a single stockpile has been addressed for storing excess low-grade material based on the availability of processing capacity and for possible future processing. The proposed scheduling model maximizes the project NPV while respecting a series of physical and economic constraints. Differently from common practice, where deterministic models are used to calculate the average grade for material in the stockpiles, in this work a stochastic approach was performed, starting from the time of planning before the stockpile realization. By performing a probability analysis on two case studies (on iron and gold deposits), it was proven that the stockpile attributes can be treated as normally distributed random variables. Afterwards, the stochastic programming model was formulated in an open pit gold mine in order to determine the optimum amount of ore dispatched from different bench levels in the open pit and at the same time a low-grade stockpile to the mill. The chance-constrained programming was finally applied to obtain the equivalent deterministic solution of the primary model. The obtained results have shown a better feed grade for the processing plant with a higher NPV and probability of grade blending constraint satisfaction, with respect to using the traditional stockpile deterministic model.

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RADZKI, Grzegorz, Amila THIBBOTUWAWA i Grzegorz BOCEWICZ. "UAVS FLIGHT ROUTES OPTIMIZATION IN CHANGING WEATHER CONDITIONS – CONSTRAINT PROGRAMMING APPROACH". Applied Computer Science 15, nr 3 (30.09.2019): 5–20. http://dx.doi.org/10.35784/acs-2019-17.

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The problem of delivering goods in a distribution network is considered in which a fleet of Unmanned Aerial Vehicles (UAV) carries out transport operations. The changing weather conditions in which the transport operations take place and the UAVs energy capacity levels influenced by the weather conditions are taken into account as factors that affect the determination of a collision-free route. The goods must be delivered to the customers in a given time window. Establishing the routes are the focus of this study. Solutions maximizing the level of customer satisfaction are focused and the computational experiments presented in the study show the impact of weather conditions on route determination.

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Jeavons, P., i J. Petke. "Local Consistency and SAT-Solvers". Journal of Artificial Intelligence Research 43 (19.03.2012): 329–51. http://dx.doi.org/10.1613/jair.3531.

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Local consistency techniques such as k-consistency are a key component of specialised solvers for constraint satisfaction problems. In this paper we show that the power of using k-consistency techniques on a constraint satisfaction problem is precisely captured by using a particular inference rule, which we call negative-hyper-resolution, on the standard direct encoding of the problem into Boolean clauses. We also show that current clause-learning SAT-solvers will discover in expected polynomial time any inconsistency that can be deduced from a given set of clauses using negative-hyper-resolvents of a fixed size. We combine these two results to show that, without being explicitly designed to do so, current clause-learning SAT-solvers efficiently simulate k-consistency techniques, for all fixed values of k. We then give some experimental results to show that this feature allows clause-learning SAT-solvers to efficiently solve certain families of constraint problems which are challenging for conventional constraint-programming solvers.

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Pothitos, Nikolaos, i Panagiotis Stamatopoulos. "Building Search Methods with Self-Confidence in a Constraint Programming Library". International Journal on Artificial Intelligence Tools 27, nr 04 (czerwiec 2018): 1860003. http://dx.doi.org/10.1142/s0218213018600035.

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In the late 1990s, Constrained Programming (CP) promised to separate the declaration of a problem from the process to solve it. This work attempts to serve this direction, by implementing and presenting a modular way to define search methods that seek solutions to arbitrary Constraint Satisfaction Problems (CSPs). The user just declares their CSP, and it can be solved using a portfolio of search methods already in place. Apart from the pluggable search methods framework for any CSP, we also introduce pluggable heuristics for our search methods. We found an efficient stochastic heuristics’ paradigm that smoothly combines randomness with normal heuristics. We consider a factor of disobedience to normal heuristics, and we fine-tune it each time, according to our estimation of normal heuristics’ reliability (confidence). We prove mathematically that while the disobedience factor decreases, the stochastic heuristics approximate deterministic normal heuristics. Our algebraic evidence is supported by empirical evaluations on real life problems: A new search method, namely PoPS, that exploits this heuristics’ paradigm, can outperform regular well-known constructive search methods.

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