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Journal articles on the topic 'Constraints satisfaction problem'

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Published: 4 June 2021

Last updated: 8 June 2024

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1

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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SCHIEX, THOMAS, and GÉRARD VERFAILLIE. "NOGOOD RECORDING FOR STATIC AND DYNAMIC CONSTRAINT SATISFACTION PROBLEMS." International Journal on Artificial Intelligence Tools 03, no. 02 (1994): 187–207. http://dx.doi.org/10.1142/s0218213094000108.

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Many AI synthesis problems such as planning, scheduling or design may be encoded in a constraint satisfaction problems (CSP). A CSP is typically defined as the problem of finding any consistent labeling for a fixed set of variables satisfying all given constraints between these variables. However, for many real tasks, the set of constraints to consider may evolve because of the environment or because of user interactions. The notion of dynamic CSP (DCSP) [DD88] has been proposed to represent such evolutions. The problem we consider here is the solution maintenance problem in a DCSP. Naively ap

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Adil, Bouhouch, Er-Rafyg Aicha, and Ez-Zahout Abderrahmane. "Neural network to solve fuzzy constraint satisfaction problems." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 1 (2024): 228. http://dx.doi.org/10.11591/ijai.v13.i1.pp228-235.

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<p>It has been proven that solving the constraint satisfaction problem (CSP) is an No Polynomial hard combinatorial optimization problem. This holds true even in cases where the constraints are fuzzy, known as fuzzy constraint satisfaction problems (FCSP). Therefore, the continuous Hopfield neural network model can be utilized to resolve it. The original algorithm was developed by Talaavan in 2005. Many practical problems can be represented as a FCSP. In this paper, we expand on a neural network technique that was initially developed for solving CSP and adapt it to tackle problems that i

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Mnif, Mouna Gargouri, and Sadok Bouamama. "Multi-Layer Distributed Constraint Satisfaction for Multi-criteria Optimization Problem." International Journal of Applied Metaheuristic Computing 11, no. 2 (2020): 134–55. http://dx.doi.org/10.4018/ijamc.2020040107.

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This article introduces a new approach to solve the multimodal transportation network planning problem (MTNP). In this problem, the commodities must be transported from an international network by at least two different transport modes. The main purpose is to identify the best multimodal transportation strategy. The present contribution focuses on efficient optimization methods to solve MTNP. This includes the assignment and the scheduling problems. The authors split the MTNP into layered. Each layer is presented by an agent. These agents interact, collaborate, and communicate together to solv

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Jonsson, Peter, Victor Lagerkvist, and Sebastian Ordyniak. "Computational Short Cuts in Infinite Domain Constraint Satisfaction." Journal of Artificial Intelligence Research 75 (November 16, 2022): 793–831. http://dx.doi.org/10.1613/jair.1.13787.

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A backdoor in a finite-domain CSP instance is a set of variables where each possible instantiation moves the instance into a polynomial-time solvable class. Backdoors have found many applications in artificial intelligence and elsewhere, and the algorithmic problem of finding such backdoors has consequently been intensively studied. Sioutis and Janhunen have proposed a generalised backdoor concept suitable for infinite-domain CSP instances over binary constraints. We generalise their concept into a large class of CSPs that allow for higher-arity constraints. We show that this kind of infinite-

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ZHANG, YUANLIN, and ROLAND H. C. YAP. "Solving functional constraints by variable substitution." Theory and Practice of Logic Programming 11, no. 2-3 (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 sol

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Chand, A. "A heuristic approach to constraint optimization in timetabling." South Pacific Journal of Natural and Applied Sciences 20, no. 1 (2002): 64. http://dx.doi.org/10.1071/sp02013.

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Timetabling is a difficult (NP-complete) problem and belongs to a general class of problems known as scheduling. Due to a variety of constraints typical in different timetabling environments, it has been difficult to develop a generic solution for timetabling. This paper is an attempt to define a generic computational model for examination timetabling for predefined constraints found in the problem, and proposes a heuristic method of developing an acceptable solution. The declarative nature of the developed constraints language (based on the structured query language) is utilized to construct

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Rutishauser, Ueli, Jean-Jacques Slotine, and Rodney J. Douglas. "Solving Constraint-Satisfaction Problems with Distributed Neocortical-Like Neuronal Networks." Neural Computation 30, no. 5 (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 t

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Abbasian, Reza, and Malek Mouhoub. "A New Parallel GA-Based Method for Constraint Satisfaction Problems." International Journal of Computational Intelligence and Applications 15, no. 03 (2016): 1650017. http://dx.doi.org/10.1142/s1469026816500176.

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Despite some success of Genetic Algorithms (GAs) when tackling Constraint Satisfaction Problems (CSPs), they generally suffer from poor crossover operators. In order to overcome this limitation in practice, we propose a novel crossover specifically designed for solving CSPs including Temporal CSPs (TCSPs). Together with a variable ordering heuristic and an integration into a parallel architecture, this proposed crossover enables the solving of large and hard problem instances as demonstrated by the experimental tests conducted on randomly generated CSPs and TCSPs based on the model RB. We will

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Lakmazaheri, Sivand. "Constraint-based reasoning via Grobner Bases." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 11, no. 1 (1997): 5–15. http://dx.doi.org/10.1017/s0890060400001803.

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AbstractConstraint-based reasoning is a problem-solving approach based on deductive reasoning. In this approach, a problem is modeled in terms of hypotheses and conclusion constraints, and it is solved via constraint satisfaction. The ability to handle linear and nonlinear algebraic constraints is essential for successful application of constraint-based reasoning in engineering. Due to the scarcity of algebraic techniques for satisfying nonlinear constraints, little attention has been paid to the use of constraint-based reasoning for solving nonlinear problems. This paper examines the use of t

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Kumar, Mohit, Samuel Kolb, Clement Gautrais, and Luc De Raedt. "Democratizing Constraint Satisfaction Problems through Machine Learning." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 18 (2021): 16057–59. http://dx.doi.org/10.1609/aaai.v35i18.18011.

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Constraint satisfaction problems (CSPs) are used widely, especially in the field of operations research, to model various real world problems like scheduling or planning. However,modelling a problem as a CSP is not trivial, it is labour intensive and requires both modelling and domain expertise. The emerging field of constraint learning deals with this problem by automatically learning constraints from a given dataset. While there are several interesting approaches for constraint learning, these works are hard to access for a non-expert user. Furthermore, different approaches have different un

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Chevalier, Aline, and Julien Cegarra. "A Constraint-Satisfaction Approach to Studying the Management of Multiple Objectives in Design Problem-Solving." Psychological Reports 99, no. 2 (2006): 488–93. http://dx.doi.org/10.2466/pr0.99.2.488-493.

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How professional and novice designers manage multiple objectives through constraint satisfaction was studied. An objective is defined as a network of constraints; a constraint is characterized as related to a source (internal or external) and an addressee (e.g., client or user). It has been shown that designers favour objectives related to external constraint (e.g., data from the task) and to the client. Generally, client and external constraints are identical. To study the management of multiple objectives, fifteen web site designers were instructed to design a site according to user's object

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Relich, Marcin. "An evaluation of project completion with application of fuzzy set theory." Management 16, no. 1 (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 pro

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Ligęza, Antoni. "Models and Tools for Improving Efficiency in Constraint Logic Programming." Decision Making in Manufacturing and Services 5, no. 1 (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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MOUHOUB, MALEK. "A HOPFIELD-TYPE NEURAL NETWORK BASED MODEL FOR TEMPORAL CONSTRAINTS." International Journal on Artificial Intelligence Tools 13, no. 03 (2004): 533–45. http://dx.doi.org/10.1142/s0218213004001673.

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In this paper we present an approximation method based on discrete Hopfield neural network (DHNN) for solving temporal constraint satisfaction problems. This method is of interest for problems involving numeric and symbolic temporal constraints and where a solution satisfying the constraints of the problem needs to be found within a given deadline. More precisely the method has the ability to provide a solution with a quality proportional to the allocated process time. The quality of the solution corresponds here to the number of satisfied constraints. This property is very important for real

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Goyal, Kshitij, Sebastijan Dumancic, and Hendrik Blockeel. "DeepSaDe: Learning Neural Networks That Guarantee Domain Constraint Satisfaction." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 11 (2024): 12199–207. http://dx.doi.org/10.1609/aaai.v38i11.29109.

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As machine learning models, specifically neural networks, are becoming increasingly popular, there are concerns regarding their trustworthiness, specially in safety-critical applications, e.g. actions of an autonomous vehicle must be safe. There are approaches that can train neural networks where such domain requirements are enforced as constraints, but they either cannot guarantee that the constraint will be satisfied by all possible predictions (even on unseen data) or they are limited in the type of constraints that can be enforced. In this paper, we present an approach to train neural netw

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Bhansali, S., G. A. Kramer, and T. J. Hoar. "A Principled Approach Towards Symbolic Geometric Constraint Satisfaction." Journal of Artificial Intelligence Research 4 (June 1, 1996): 419–43. http://dx.doi.org/10.1613/jair.292.

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An important problem in geometric reasoning is to find the configuration of a collection of geometric bodies so as to satisfy a set of given constraints. Recently, it has been suggested that this problem can be solved efficiently by symbolically reasoning about geometry. This approach, called degrees of freedom analysis, employs a set of specialized routines called plan fragments that specify how to change the configuration of a set of bodies to satisfy a new constraint while preserving existing constraints. A potential drawback, which limits the scalability of this approach, is concerned with

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WESTFOLD, STEPHEN J., and DOUGLAS R. SMITH. "Synthesis of efficient constraint-satisfaction programs." Knowledge Engineering Review 16, no. 1 (2001): 69–84. http://dx.doi.org/10.1017/s0269888901000029.

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In this paper we describe the framework we have developed in KIDS (Kestrel Interactive Development System) for generating efficient constraint satisfaction programs. We have used KIDS to synthesise global search scheduling programs that have proved to be dramatically faster than other programs running the same data. We focus on the underlying ideas that lead to this efficiency. The key to the efficiency is the reduction of the size of the search space by an effective representation of sets of possible solutions (solution spaces) that allows efficient constraint propagation and pruning at the l

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Cohen, D., J. Crampton, A. Gagarin, G. Gutin, and M. Jones. "Iterative Plan Construction for the Workflow Satisfiability Problem." Journal of Artificial Intelligence Research 51 (November 21, 2014): 555–77. http://dx.doi.org/10.1613/jair.4435.

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The Workflow Satisfiability Problem (WSP) is a problem of practical interest that arises whenever tasks need to be performed by authorized users, subject to constraints defined by business rules. We are required to decide whether there exists a plan - an assignment of tasks to authorized users - such that all constraints are satisfied. It is natural to see the WSP as a subclass of the Constraint Satisfaction Problem (CSP) in which the variables are tasks and the domain is the set of users. What makes the WSP distinctive is that the number of tasks is usually very small compared to the number o

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SAMAL, ASHOK, and TOM HENDERSON. "PARALLEL SPLIT-LEVEL RELAXATION." International Journal of Pattern Recognition and Artificial Intelligence 02, no. 03 (1988): 425–42. http://dx.doi.org/10.1142/s021800148800025x.

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The goal of high level vision is to identify a set of regions in a given image. This has been called by various names: the scene labeling problem’, the consistent labeling problem2, the constraint satisfaction problem3, Waltz filtering4, the satisfying assignment problem5, etc. There are several approaches to solve this problem, including backtracking, graph matching and relaxation. A new method called split-level relaxation, which is based on discrete relaxation was proposed in Ref. 6. It takes care of multiple semantic constraints by considering each of them independently. The problem is kno

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VU, XUAN-HA, and BARRY O'SULLIVAN. "A UNIFYING FRAMEWORK FOR GENERALIZED CONSTRAINT ACQUISITION." International Journal on Artificial Intelligence Tools 17, no. 05 (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

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Lee, Jimmy, and K. Leung. "A Stronger Consistency for Soft Global Constraints in Weighted Constraint Satisfaction." Proceedings of the AAAI Conference on Artificial Intelligence 24, no. 1 (2010): 121–27. http://dx.doi.org/10.1609/aaai.v24i1.7550.

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Weighted Constraint Satisfaction is made practical by powerful consistency techniques, such as AC*, FDAC* and EDAC*, which reduce search space effectively and efficiently during search, but they are designed for only binary and ternary constraints. To allow soft global constraints, usually of high arity, to enjoy the same benefits, Lee and Leung give polynomial time algorithms to enforce generalized AC* (GAC*) and FDAC* (FDGAC*) for projection-safe soft non-binary constraints. Generalizing the stronger EDAC* is less straightforward. In this paper, we first reveal the oscillation problem when e

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Lee, Jimmy H. M., and Allen Z. Zhong. "Exploiting Functional Constraints in Automatic Dominance Breaking for Constraint Optimization." Journal of Artificial Intelligence Research 78 (September 13, 2023): 1–35. http://dx.doi.org/10.1613/jair.1.14714.

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Dominance breaking is a powerful technique in improving the solving efficiency of Constraint Optimization Problems (COPs) by removing provably suboptimal solutions with additional constraints. While dominance breaking is effective in a range of practical problems, it is usually problem specific and requires human insights into problem structures to come up with correct dominance breaking constraints. Recently, a framework is proposed to generate nogood constraints automatically for dominance breaking, which formulates nogood generation as solving auxiliary Constraint Satisfaction Problems (CSP

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Xu, T. Y., and X. S. Qin. "Applying an Extended Fuzzy Parametric Approach to the Problem of Water Allocations." Mathematical Problems in Engineering 2013 (2013): 1–10. http://dx.doi.org/10.1155/2013/647569.

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An extended fuzzy parametric programming (EFPP) model was proposed for supporting water resources allocation problems under uncertainty. EFPP deals with flexible constraints (i.e., fuzzy relationships) by allowing violation of constraints at certain satisfaction degrees (i.e.,αlevels) and employs fuzzy ranking method to handle trapezoidal-shaped fuzzy coefficients. The objective function is defuzzified by usingβcuts and weighting factors. The applicability of EFPP was demonstrated by a numerical example and a water resources allocation case. A series of decision alternatives at various satisfa

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Juan, Yeh-Chun, and Yi-Rui Peng. "A Constraint Satisfaction Coordination Approach for Distributed Supply Chain Production Planning." Asia-Pacific Journal of Operational Research 31, no. 06 (2014): 1450041. http://dx.doi.org/10.1142/s0217595914500419.

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Currently, distributed supply chain production planning (SCPP) is a practical problem affecting global supply chain production. Distributed SCPP is a complex constraint satisfaction problem (CSP) comprising a series of constraints intra- and inter-production members on orders, capacity, and materials. Previous studies have proposed various advanced planning and scheduling (APS) algorithms for companies to solve internal CSPs related to production planning. Distributed SCPP problems additionally require a coordination approach to resolve conflicts among the production plans of individual produc

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Dabrowski, Konrad K., Peter Jonsson, Sebastian Ordyniak, and George Osipov. "Solving Infinite-Domain CSPs Using the Patchwork Property." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 5 (2021): 3715–23. http://dx.doi.org/10.1609/aaai.v35i5.16488.

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The constraint satisfaction problem (CSP) has important applications in computer science and AI. In particular, infinite-domain CSPs have been intensively used in subareas of AI such as spatio-temporal reasoning. Since constraint satisfaction is a computationally hard problem, much work has been devoted to identifying restricted problems that are efficiently solvable. One way of doing this is to restrict the interactions of variables and constraints, and a highly successful approach is to bound the treewidth of the underlying primal graph. Bodirsky & Dalmau [J. Comput. System. Sci., 79(1),

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Liu, Cheng, and Kun Kun Gu. "Location Problems of the Distribution Center under Uncertain Environment." Advanced Materials Research 171-172 (December 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 pr

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Zuenko, Aleksandr A., and Olga V. Fridman. "Reasoning with temporal constraints." Transactions of the Kоla Science Centre of RAS. Series: Engineering Sciences 14, no. 7/2023 (2024): 43–51. http://dx.doi.org/10.37614/2949-1215.2023.14.7.005.

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The work deals with the organization of temporal reasoning basing on constraint satisfaction methods. The definition of the constraint satisfaction problem and the notion of constraint consistency are given. The possibilities of epresentation of a planning problem as an interval constraint network are considered. As a mathematical apparatus for the formalization of temporal reasining, the Allen’s interval algebra is described, which main operations are composition and the intersection of temporal relations. A path consistency algorithm is given that implements one of the types of local consist

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Szeider, Stefan. "Limits of Preprocessing." Proceedings of the AAAI Conference on Artificial Intelligence 25, no. 1 (2011): 93–98. http://dx.doi.org/10.1609/aaai.v25i1.7816.

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We present a first theoretical analysis of the power of polynomial-time preprocessing for important combinatorial problems from various areas in AI. We consider problems from Constraint Satisfaction, Global Constraints, Satisfiability, Nonmonotonic and Bayesian Reasoning. We show that, subject to a complexity theoretic assumption, none of the considered problems can be reduced by polynomial-time preprocessing to a problem kernel whose size is polynomial in a structural problem parameter of the input, such as induced width or backdoor size. Our results provide a firm theoretical boundary for th

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Li, Jinyang, Yuval Moskovitch, Julia Stoyanovich, and H. V. Jagadish. "Query Refinement for Diversity Constraint Satisfaction." Proceedings of the VLDB Endowment 17, no. 2 (2023): 106–18. http://dx.doi.org/10.14778/3626292.3626295.

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Diversity, group representation, and similar needs often apply to query results, which in turn require constraints on the sizes of various subgroups in the result set. Traditional relational queries only specify conditions as part of the query predicate(s), and do not support such restrictions on the output. In this paper, we study the problem of modifying queries to have the result satisfy constraints on the sizes of multiple subgroups in it. This problem, in the worst case, cannot be solved in polynomial time. Yet, with the help of provenance annotation, we are able to develop a query refine

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Kowal G., Agnieszka, Manuel R. Arahal, Cristina Martin, and Federico Barrero. "Constraint Satisfaction in Current Control of a Five-Phase Drive with Locally Tuned Predictive Controllers." Energies 12, no. 14 (2019): 2715. http://dx.doi.org/10.3390/en12142715.

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The problem of control of stator currents in multi-phase induction machines has recently been tackled by direct digital model predictive control. Although these predictive controllers can directly incorporate constraints, most reported applications for stator current control of drives do no use this possibility, being the usual practice tuning the controller to achieve the particular compromise solution. The proposal of this paper is to change the form of the tuning problem of predictive controllers so that constraints are explicitly taken into account. This is done by considering multiple con

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Relich, Marcin, and Zbigniew Banaszak. "Reference Model of Project Prototyping Problem." Foundations of Management 3, no. 1 (2011): 33–46. http://dx.doi.org/10.2478/v10238-012-0034-7.

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Reference Model of Project Prototyping ProblemThe paper presents the idea of reference model of project prototyping problem for the projects that are at risk of failure. The hierarchical structure of declarative model connects two fields: functionalities of a typical service enterprise and management system of project execution in the enterprise. The functionalities as separate Constraints Satisfaction Problems (CSP) are described. CSP contains the sets of decision variables, their domains and constraints, which link these variables. The separated problems described as CSP, then in single main

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Cooper, Martin, and Guillaume Escamocher. "A Dichotomy for 2-Constraint Forbidden CSP Patterns." Proceedings of the AAAI Conference on Artificial Intelligence 26, no. 1 (2021): 464–70. http://dx.doi.org/10.1609/aaai.v26i1.8131.

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Novel tractable classes of the binary CSP (constraint satisfaction problem) have recently been discovered by studying classes of instances defined by excluding subproblems described by patterns. The complete characterisation of all tractable classes defined by forbidden patterns is a challenging problem. We demonstrate a dichotomy in the case of forbidden patterns consisting of two constraints.

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SADAOUI, SAMIRA, MALEK MOUHOUB, and BO CHEN. "AN EFFICIENT LOTOS-BASED FRAMEWORK FOR DESCRIBING AND SOLVING (TEMPORAL) CSPs." International Journal of Software Engineering and Knowledge Engineering 19, no. 06 (2009): 765–89. http://dx.doi.org/10.1142/s0218194009004416.

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Simulation of complex Lotos specifications is not always efficient due to the space explosion problem of their corresponding transition systems. To overcome this difficulty in practice, we present in this paper a novel approach which integrates constraint propagation techniques into the Lotos specifications. These solving techniques are used to reduce the size of the search space before and during the search for a solution to a given combinatorial problem under constraints. In order to do that, we first tackle the challenging task of describing combinatorial problems in Lotos using the Constra

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Paparrizou, Anastasia. "Efficient Algorithms for Strong Local Consistencies in Constraint Satisfaction Problems." Proceedings of the AAAI Conference on Artificial Intelligence 27, no. 1 (2013): 1674–75. http://dx.doi.org/10.1609/aaai.v27i1.8504.

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The existing complete methods for solving Constraint Satisfaction Problems (CSPs) are usually based on a combination of exhaustive search and constraint propagation techniques for the reduction of the search space. Such propagation techniques are the local consistency algorithms. Arc Consistency (AC) and Generalized Arc Consistency (GAC) are the most widely studied local consistencies that are predominantly used in constraint solvers. However, many stronger local consistencies than (G)AC have been proposed, even recently, but have been rather overlooked due to their prohibitive cost. This rese

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Finkel, Raphael, and Barry O'Sullivan. "Reasoning about conditional constraint specification problems and feature models." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 25, no. 2 (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

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Song, Hong Xi, Jian Xin Zhang, Kun Gao, Wei Meng, and Li Cheng. "Optimum Charge Plan of Steelmaking Continuous Casting Based on the Modified Multi-Objective Evolutionary Algorithm." Advanced Materials Research 694-697 (May 2013): 3406–11. http://dx.doi.org/10.4028/www.scientific.net/amr.694-697.3406.

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The charge plan is a basic problem of the metallurgical industry. Through a comprehensive consideration of the process constraints and production costs, charge plan is a typical multi-objective optimization problem. A multi-objective optimization model of charge plan was built in this paper. As direct encoding is hard to be used in the many constraints problem case, a new method of generate feasible solutions based on constraint satisfaction was proposed. A novel adaptive grid algorithm was also proposed to improve the current multi-objective evolutionary algorithm’s population maintain strate

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Bouhmala, N. "A Variable Depth Search Algorithm for Binary Constraint Satisfaction Problems." Mathematical Problems in Engineering 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/637809.

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The constraint satisfaction problem (CSP) is a popular used paradigm to model a wide spectrum of optimization problems in artificial intelligence. This paper presents a fast metaheuristic for solving binary constraint satisfaction problems. The method can be classified as a variable depth search metaheuristic combining a greedy local search using a self-adaptive weighting strategy on the constraint weights. Several metaheuristics have been developed in the past using various penalty weight mechanisms on the constraints. What distinguishes the proposed metaheuristic from those developed in the

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Zuenko, Alexander, Yurii Oleynik, and Roman Makedonov. "A method for solving the open-pit mine production scheduling problem using the constraint programming paradigm." Journal of Physics: Conference Series 2060, no. 1 (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, t

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Nagarajan, S., S. D. Goodwin, and A. Sattar. "Extending Dual Arc Consistency." International Journal of Pattern Recognition and Artificial Intelligence 17, no. 05 (2003): 781–815. http://dx.doi.org/10.1142/s0218001403002654.

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Many extensions to existing binary constraint satisfaction algorithms have been proposed that directly deal with nonbinary constraints. Another choice is to perform a structural transformation of the representation of the problem, so that the resulting problem is a binary CSP except that now the original constraints which were nonbinary are replaced by binary compatibility constraints between relations. A lot of recent work has focussed on comparing different levels of local consistency enforceable in the nonbinary representation with the dual representation. In this paper we present extension

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Bodirsky, Manuel, Hubie Chen, and Michał Wrona. "Tractability of quantified temporal constraints to the max." International Journal of Algebra and Computation 24, no. 08 (2014): 1141–56. http://dx.doi.org/10.1142/s0218196714500507.

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A temporal constraint language is a set of relations that are first-order definable over (ℚ;<). We show that several temporal constraint languages whose constraint satisfaction problem is maximally tractable are also maximally tractable for the more expressive quantified constraint satisfaction problem. These constraint languages are defined in terms of preservation under certain binary polymorphisms. We also present syntactic characterizations of the relations in these languages.

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