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Добірка наукової літератури з теми "Constraint networks"

Автор: Grafiati
Опубліковано: 11 березня 2023

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Статті в журналах з теми "Constraint networks"

1

Brosowsky, Mathis, Florian Keck, Olaf Dünkel, and Marius Zöllner. "Sample-Specific Output Constraints for Neural Networks." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 8 (May 18, 2021): 6812–21. http://dx.doi.org/10.1609/aaai.v35i8.16841.

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Анотація:
It is common practice to constrain the output space of a neural network with the final layer to a problem-specific value range. However, for many tasks it is desired to restrict the output space for each input independently to a different subdomain with a non-trivial geometry, e.g. in safety-critical applications, to exclude hazardous outputs sample-wise. We propose ConstraintNet—a scalable neural network architecture which constrains the output space in each forward pass independently. Contrary to prior approaches, which perform a projection in the final layer, ConstraintNet applies an input-dependent parametrization of the constrained output space. Thereby, the complete interior of the constrained region is covered and computational costs are reduced significantly. For constraints in form of convex polytopes, we leverage the vertex representation to specify the parametrization. The second modification consists of adding an auxiliary input in form of a tensor description of the constraint to enable the handling of multiple constraints for the same sample. Finally, ConstraintNet is end-to-end trainable with almost no overhead in the forward and backward pass. We demonstrate ConstraintNet on two regression tasks: First, we modify a CNN and construct several constraints for facial landmark detection tasks. Second, we demonstrate the application to a follow object controller for vehicles and accomplish safe reinforcement learning in this case. In both experiments, ConstraintNet improves performance and we conclude that our approach is promising for applying neural networks in safety-critical environments.
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2

Rong, Zihao, Shaofan Wang, Dehui Kong, and Baocai Yin. "Improving object detection quality with structural constraints." PLOS ONE 17, no. 5 (May 18, 2022): e0267863. http://dx.doi.org/10.1371/journal.pone.0267863.

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Recent researches revealed object detection networks using the simple “classification loss + localization loss” training objective are not effectively optimized in many cases, while providing additional constraints on network features could effectively improve object detection quality. Specifically, some works used constraints on training sample relations to successfully learn discriminative network features. Based on these observations, we propose Structural Constraint for improving object detection quality. Structural constraint supervises feature learning in classification and localization network branches with Fisher Loss and Equi-proportion Loss respectively, by requiring feature similarities of training sample pairs to be consistent with corresponding ground truth label similarities. Structural constraint could be applied to all object detection network architectures with the assist of our Proxy Feature design. Our experiment results showed that structural constraint mechanism is able to optimize object class instances’ distribution in network feature space, and consequently detection results. Evaluations on MSCOCO2017 and KITTI datasets showed that our structural constraint mechanism is able to assist baseline networks to outperform modern counterpart detectors in terms of object detection quality.
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3

Zhang, Y., and R. H. C. Yap. "Set Intersection and Consistency in Constraint Networks." Journal of Artificial Intelligence Research 27 (December 13, 2006): 441–64. http://dx.doi.org/10.1613/jair.2058.

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In this paper, we show that there is a close relation between consistency in a constraint network and set intersection. A proof schema is provided as a generic way to obtain consistency properties from properties on set intersection. This approach not only simplifies the understanding of and unifies many existing consistency results, but also directs the study of consistency to that of set intersection properties in many situations, as demonstrated by the results on the convexity and tightness of constraints in this paper. Specifically, we identify a new class of tree convex constraints where local consistency ensures global consistency. This generalizes row convex constraints. Various consistency results are also obtained on constraint networks where only some, in contrast to all in the existing work,constraints are tight.
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4

Kharroubi, Idris, Thomas Lim, and Xavier Warin. "Discretization and machine learning approximation of BSDEs with a constraint on the Gains-process." Monte Carlo Methods and Applications 27, no. 1 (January 15, 2021): 27–55. http://dx.doi.org/10.1515/mcma-2020-2080.

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Abstract We study the approximation of backward stochastic differential equations (BSDEs for short) with a constraint on the gains process. We first discretize the constraint by applying a so-called facelift operator at times of a grid. We show that this discretely constrained BSDE converges to the continuously constrained one as the mesh grid converges to zero. We then focus on the approximation of the discretely constrained BSDE. For that we adopt a machine learning approach. We show that the facelift can be approximated by an optimization problem over a class of neural networks under constraints on the neural network and its derivative. We then derive an algorithm converging to the discretely constrained BSDE as the number of neurons goes to infinity. We end by numerical experiments.
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5

Dechter, Rina, Itay Meiri, and Judea Pearl. "Temporal constraint networks." Artificial Intelligence 49, no. 1-3 (May 1991): 61–95. http://dx.doi.org/10.1016/0004-3702(91)90006-6.

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6

Msaaf, Mohammed, and Fouad Belmajdoub. "Diagnosis of Discrete Event Systems under Temporal Constraints Using Neural Network." International Journal of Engineering Research in Africa 49 (June 2020): 198–205. http://dx.doi.org/10.4028/www.scientific.net/jera.49.198.

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Анотація:
The good functioning of a discrete event system is related to how much the temporal constraints are respected. This paper gives a new approach, based on a statistical model and neural network, that allows the verification of temporal constraints in DES. We will perform an online temporal constraint checking which can detect in real time any abnormal functioning related to the violation of a temporal constraint. In the first phase, the construction of temporal constraints from statistical model is shown and after that neural networks are involved in dealing with the online temporal constraint checking.
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7

Wang, Xiao Fei, Xi Zhang, Yue Bing Chen, Lei Zhang, and Chao Jing Tang. "Spectrum Assignment Algorithm Based on Clonal Selection in Cognitive Radio Networks." Advanced Materials Research 457-458 (January 2012): 931–39. http://dx.doi.org/10.4028/www.scientific.net/amr.457-458.931.

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An improved-immune-clonal-selection based spectrum assignment algorithm (IICSA) in cognitive radio networks is proposed, combing graph theory and immune optimization. It uses constraint satisfaction operation to make encoded antibody population satisfy constraints, and realizes the global optimization. The random-constraint satisfaction operator and fair-constraint satisfaction operator are designed to guarantee efficiency and fairness, respectively. Simulations are performed for performance comparison between the IICSA and the color-sensitive graph coloring algorithm. The results indicate that the proposed algorithm increases network utilization, and efficiently improves the fairness.
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8

Buscema, Massimo. "Constraint Satisfaction Neural Networks." Substance Use & Misuse 33, no. 2 (January 1998): 389–408. http://dx.doi.org/10.3109/10826089809115873.

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9

Suter, D. "Constraint networks in vision." IEEE Transactions on Computers 40, no. 12 (1991): 1359–67. http://dx.doi.org/10.1109/12.106221.

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10

Gottlob, Georg. "On minimal constraint networks." Artificial Intelligence 191-192 (November 2012): 42–60. http://dx.doi.org/10.1016/j.artint.2012.07.006.

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Дисертації з теми "Constraint networks"

1

Beaumont, Matthew, and n/a. "Handling Over-Constrained Temporal Constraint Networks." Griffith University. School of Information Technology, 2004. http://www4.gu.edu.au:8080/adt-root/public/adt-QGU20041213.084512.

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Анотація:
Temporal reasoning has been an active research area for over twenty years, with most work focussing on either enhancing the efficiency of current temporal reasoning algorithms or enriching the existing algebras. However, there has been little research into handling over-constrained temporal problems except to recognise that a problem is over-constrained and then to terminate. As many real-world temporal reasoning problems are inherently over-constrained, particularly in the scheduling domain, there is a significant need for approaches that can handle over-constrained situations. In this thesis, we propose two backtracking algorithms to gain partial solutions to over-constrained temporal problems. We also propose a new representation, the end-point ordering model, to allow the use of local search algorithms for temporal reasoning. Using this model we propose a constraint weighting local search algorithm as well as tabu and random-restart algorithms to gain partial solutions to over-constrained temporal problems. Specifically, the contributions of this thesis are: The introduction and empirical evaluation of two backtracking algorithms to solve over-constrained temporal problems. We provide two backtracking algorithms to close the gap in current temporal research to solve over-constrained problems; The representation of temporal constraint networks using the end-point ordering model. As current representation models are not suited for local search algorithms, we develop a new model such that local search can be applied efficiently to temporal reasoning; The development of a constraint weighting local search algorithm for under-constrained problems. As constraint weighting has proven to be efficient for solving many CSP problems, we implement a constraint weighting algorithm to solve under-constrained temporal problems; An empirical evaluation of constraint weighting local search against traditional backtracking algorithms. We compare the results of a constraint weighting algorithm with traditional backtracking approaches and find that in many cases constraint weighting has superior performance; The development of a constraint weighting local search, tabu search and random-restart local search algorithm for over-constrained temporal problems. We extend our constraint weighting algorithm to solve under-constrained temporal problems as well as implement two other popular local search algorithms: tabu search and random-restart; An empirical evaluation of all three local search algorithms against the two backtracking algorithms. We compare the results of all three local search algorithms with our twobacktracking algorithms for solving over-constrained temporal reasoning problems and find that local search proves to be considerably superior.
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2

Beaumont, Matthew. "Handling Over-Constrained Temporal Constraint Networks." Thesis, Griffith University, 2004. http://hdl.handle.net/10072/366603.

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Анотація:
Temporal reasoning has been an active research area for over twenty years, with most work focussing on either enhancing the efficiency of current temporal reasoning algorithms or enriching the existing algebras. However, there has been little research into handling over-constrained temporal problems except to recognise that a problem is over-constrained and then to terminate. As many real-world temporal reasoning problems are inherently over-constrained, particularly in the scheduling domain, there is a significant need for approaches that can handle over-constrained situations. In this thesis, we propose two backtracking algorithms to gain partial solutions to over-constrained temporal problems. We also propose a new representation, the end-point ordering model, to allow the use of local search algorithms for temporal reasoning. Using this model we propose a constraint weighting local search algorithm as well as tabu and random-restart algorithms to gain partial solutions to over-constrained temporal problems. Specifically, the contributions of this thesis are: The introduction and empirical evaluation of two backtracking algorithms to solve over-constrained temporal problems. We provide two backtracking algorithms to close the gap in current temporal research to solve over-constrained problems; The representation of temporal constraint networks using the end-point ordering model. As current representation models are not suited for local search algorithms, we develop a new model such that local search can be applied efficiently to temporal reasoning; The development of a constraint weighting local search algorithm for under-constrained problems. As constraint weighting has proven to be efficient for solving many CSP problems, we implement a constraint weighting algorithm to solve under-constrained temporal problems; An empirical evaluation of constraint weighting local search against traditional backtracking algorithms. We compare the results of a constraint weighting algorithm with traditional backtracking approaches and find that in many cases constraint weighting has superior performance; The development of a constraint weighting local search, tabu search and random-restart local search algorithm for over-constrained temporal problems. We extend our constraint weighting algorithm to solve under-constrained temporal problems as well as implement two other popular local search algorithms: tabu search and random-restart; An empirical evaluation of all three local search algorithms against the two backtracking algorithms. We compare the results of all three local search algorithms with our twobacktracking algorithms for solving over-constrained temporal reasoning problems and find that local search proves to be considerably superior.
Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
Institute for Integrated and Intelligent Systems
Full Text
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3

Francisco, Rodriguez Maria Andreina. "Consistency of Constraint Networks Induced by Automaton-Based Constraint Specifications." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-156441.

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Анотація:
In this work we discuss the consistency of constraints for which the set of solutions can be recognised by a deterministic finite automaton. Such an automaton induces a decomposition of the constraint into a conjunction of constraints. Since the level of filtering for the conjunction of constraints is not known, at any point during search there might be only one possible solution but, since all impossible values might not have yet been removed, we could be wasting time looking at impossible combinations of values. The so far most general result is that if the constraint hypergraph of such a decomposition is Berge-acyclic, then the decomposition provides hyper-arc consistency, which means that the decomposition achieves the best possible filtering. We focus our work on constraint networks that have alpha-acyclic, centred-cyclic or sliding-cyclic hypergraph representations. For each of these kinds of constraints networks we show systematically the necessary conditions to achieve hyper-arc consistency.
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4

Hassani, Bijarbooneh Farshid. "Constraint Programming for Wireless Sensor Networks." Doctoral thesis, Uppsala universitet, Avdelningen för datalogi, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-241378.

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Анотація:
In recent years, wireless sensor networks (WSNs) have grown rapidly and have had a substantial impact in many applications. A WSN is a network that consists of interconnected autonomous nodes that monitor physical and environmental conditions, such as temperature, humidity, pollution, etc. If required, nodes in a WSN can perform actions to affect the environment. WSNs present an interesting and challenging field of research due to the distributed nature of the network and the limited resources of the nodes. It is necessary for a node in a WSN to be small to enable easy deployment in an environment and consume as little energy as possible to prolong its battery lifetime. There are many challenges in WSNs, such as programming a large number of nodes, designing communication protocols, achieving energy efficiency, respecting limited bandwidth, and operating with limited memory. WSNs are further constrained due to the deployment of the nodes in indoor and outdoor environments and obstacles in the environment. In this dissertation, we study some of the fundamental optimisation problems related to the programming, coverage, mobility, data collection, and data loss of WSNs, modelled as standalone optimisation problems or as optimisation problems integrated with protocol design. Our proposed solution methods come from various fields of research including constraint programming, integer linear programming, heuristic-based algorithms, and data inference techniques.
ProFuN
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5

Draghici, Sorin. "Using constraints to improve generalisation and training of feedforward neural networks : constraint based decomposition and complex backpropagation." Thesis, University of St Andrews, 1996. http://hdl.handle.net/10023/13467.

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Neural networks can be analysed from two points of view: training and generalisation. The training is characterised by a trade-off between the 'goodness' of the training algorithm itself (speed, reliability, guaranteed convergence) and the 'goodness' of the architecture (the difficulty of the problems the network can potentially solve). Good training algorithms are available for simple architectures which cannot solve complicated problems. More complex architectures, which have been shown to be able to solve potentially any problem do not have in general simple and fast algorithms with guaranteed convergence and high reliability. A good training technique should be simple, fast and reliable, and yet also be applicable to produce a network able to solve complicated problems. The thesis presents Constraint Based Decomposition (CBD) as a technique which satisfies the above requirements well. CBD is shown to build a network able to solve complicated problems in a simple, fast and reliable manner. Furthermore, the user is given a better control over the generalisation properties of the trained network with respect to the control offered by other techniques. The generalisation issue is addressed, as well. An analysis of the meaning of the term "good generalisation" is presented and a framework for assessing generalisation is given: the generalisation can be assessed only with respect to a known or desired underlying function. The known properties of the underlying function can be embedded into the network thus ensuring a better generalisation for the given problem. This is the fundamental idea of the complex backpropagation network. This network can associate signals through associating some of their parameters using complex weights. It is shown that such a network can yield better generalisation results than a standard backpropagation network associating instantaneous values.
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6

Craven, Daniel Shawn. "A formal analysis of the MLS LAN : TCB-to-TCBE, Session Status, & TCBE-to-Session Server Protocols /." Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2004. http://library.nps.navy.mil/uhtbin/hyperion/04Sept%5FCraven.pdf.

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7

Vaez, Mohammad-Mehdi. "Nonblocking Banyan-type optical switching networks under crosstalk constraint." Diss., Georgia Institute of Technology, 1997. http://hdl.handle.net/1853/13401.

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8

Chachra, Sumit, and Theodore Elhourani. "RESOURCE ALLOCATION IN SENSOR NETWORKS USING DISTRIBUTED CONSTRAINT OPTIMIZATION." International Foundation for Telemetering, 2004. http://hdl.handle.net/10150/605299.

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International Telemetering Conference Proceedings / October 18-21, 2004 / Town & Country Resort, San Diego, California
Several algorithms have been proposed for solving constraint satisfaction and the more general constraint optimization problem in a distributed manner. In this paper we apply two such algorithms to the task of dynamic resource allocation in the sensor network domain using appropriate abstractions. The aim is to effectively track multiple targets by making the sensors coordinate with each other in a distributed manner, given a probabilistic representation of tasks (targets). We present simulation results and compare the performance of the DBA and DSA algorithms under varying experimental settings.
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9

Grigoleit, Mark Ted. "Optimisation of large scale network problems." Thesis, Curtin University, 2008. http://hdl.handle.net/20.500.11937/1405.

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Анотація:
The Constrained Shortest Path Problem (CSPP) consists of finding the shortest path in a graph or network that satisfies one or more resource constraints. Without these constraints, the shortest path problem can be solved in polynomial time; with them, the CSPP is NP-hard and thus far no polynomial-time algorithms exist for solving it optimally. The problem arises in a number of practical situations. In the case of vehicle path planning, the vehicle may be an aircraft flying through a region with obstacles such as mountains or radar detectors, with an upper bound on the fuel consumption, the travel time or the risk of attack. The vehicle may be a submarine travelling through a region with sonar detectors, with a time or risk budget. These problems all involve a network which is a discrete model of the physical domain. Another example would be the routing of voice and data information in a communications network such as a mobile phone network, where the constraints may include maximum call delays or relay node capacities. This is a problem of current economic importance, and one for which time-sensitive solutions are not always available, especially if the networks are large. We consider the simplest form of the problem, large grid networks with a single side constraint, which have been studied in the literature. This thesis explores the application of Constraint Programming combined with Lagrange Relaxation to achieve optimal or near-optimal solutions of the CSPP. The following is a brief outline of the contribution of this thesis. Lagrange Relaxation may or may not achieve optimal or near-optimal results on its own. Often, large duality gaps are present. We make a simple modification to Dijkstra’s algorithm that does not involve any additional computational work in order to generate an estimate of path time at every node.We then use this information to constrain the network along a bisecting meridian. The combination of Lagrange Relaxation (LR) and a heuristic for filtering along the meridian provide an aggressive method for finding near-optimal solutions in a short time. Two network problems are studied in this work. The first is a Submarine Transit Path problem in which the transit field contains four sonar detectors at known locations, each with the same detection profile. The side constraint is the total transit time, with the submarine capable of 2 speeds. For the single-speed case, the initial LR duality gap may be as high as 30%. The first hybrid method uses a single centre meridian to constrain the network based on the unused time resource, and is able to produce solutions that are generally within 1% of optimal and always below 3%. Using the computation time for the initial Lagrange Relaxation as a baseline, the average computation time for the first hybrid method is about 30% to 50% higher, and the worst case CPU times are 2 to 4 times higher. The second problem is a random valued network from the literature. Edge costs, times, and lengths are uniform, randomly generated integers in a given range. Since the values given in the literature problems do not yield problems with a high duality gap, the values are varied and from a population of approximately 100,000 problems only the worst 200 from each set are chosen for study. These problems have an initial LR duality gap as high as 40%. A second hybrid method is developed, using values for the unused time resource and the lower bound values computed by Dijkstra’s algorithm as part of the LR method. The computed values are then used to position multiple constraining meridians in order to allow LR to find better solutions.This second hybrid method is able to produce solutions that are generally within 0.1% of optimal, with computation times that are on average 2 times the initial Lagrange Relaxation time, and in the worst case only about 5 times higher. The best method for solving the Constrained Shortest Path Problem reported in the literature thus far is the LRE-A method of Carlyle et al. (2007), which uses Lagrange Relaxation for preprocessing followed by a bounded search using aggregate constraints. We replace Lagrange Relaxation with the second hybrid method and show that optimal solutions are produced for both network problems with computation times that are between one and two orders of magnitude faster than LRE-A. In addition, these hybrid methods combined with the bounded search are up to 2 orders of magnitude faster than the commercial CPlex package using a straightforward MILP formulation of the problem. Finally, the second hybrid method is used as a preprocessing step on both network problems, prior to running CPlex. This preprocessing reduces the network size sufficiently to allow CPlex to solve all cases to optimality up to 3 orders of magnitude faster than without this preprocessing, and up to an order of magnitude faster than using Lagrange Relaxation for preprocessing. Chapter 1 provides a review of the thesis and some terminology used. Chapter 2 reviews previous approaches to the CSPP, in particular the two current best methods. Chapter 3 applies Lagrange Relaxation to the Submarine Transit Path problem with 2 speeds, to provide a baseline for comparison. The problem is reduced to a single speed, which demonstrates the large duality gap problem possible with Lagrange Relaxation, and the first hybrid method is introduced.Chapter 4 examines a grid network problem using randomly generated edge costs and weights, and introduces the second hybrid method. Chapter 5 then applies the second hybrid method to both network problems as a preprocessing step, using both CPlex and a bounded search method from the literature to solve to optimality. The conclusion of this thesis and directions for future work are discussed in Chapter 6.
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10

Comin, Carlo. "Complexity in Infinite Games on Graphs and Temporal Constraint Networks." Doctoral thesis, Università degli studi di Trento, 2017. https://hdl.handle.net/11572/368151.

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This dissertation deals with a number of algorithmic problems motivated by automated temporal planning and formal verification of reactive and finite state systems. Particularly, we shall focus on game theoretical methods in order to obtain improved complexity bounds and faster algorithms for the following models: Hyper Temporal Networks, Conditional Simple/Hyper Temporal Networks, Conditional Simple Temporal Networks with Instantaneous Reaction Time, Update Games, Explicit McNaughton-Muller Games, Mean Payoff Games.
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Книги з теми "Constraint networks"

1

Constraint networks: Techniques and algorithms. Hoboken, NJ: ISTE/John Wiley, 2009.

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2

Yokoo, Makoto. Distributed Constraint Satisfaction: Foundations of Cooperation in Multi-agent Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001.

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3

Khan, Altaf Hamid. Feedforward neural networks with constrained weights. [s.l.]: typescript, 1996.

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4

Chandramouli, Shyam Sundar. Network Resource Allocation Under Fairness Constraints. [New York, N.Y.?]: [publisher not identified], 2014.

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5

Liu, Kun, Emilia Fridman, and Yuanqing Xia. Networked Control Under Communication Constraints. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4230-5.

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6

Zhang, Wen-An, Bo Chen, Haiyu Song, and Li Yu. Distributed Fusion Estimation for Sensor Networks with Communication Constraints. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0795-8.

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7

Beate, Lohnert, ed. Social networks: Potentials and constraints : indications from South Africa. Saarbrücken: Verlag für Entwicklungspolitik, 2007.

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8

Liu, Qinyuan, Zidong Wang, and Xiao He. Stochastic Control and Filtering over Constrained Communication Networks. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-00157-5.

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9

O'Young, D. L. Constrained heat exchanger network : targeting and design. Manchester: UMIST, 1989.

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10

Domenico, Delli Gatti, and National Bureau of Economic Research., eds. Financially constrained fluctuations in an evolving network economy. Cambridge, MA: National Bureau of Economic Research, 2008.

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Частини книг з теми "Constraint networks"

1

Tyugu, Enn, and Tarmo Uustalu. "Higher-Order Functional Constraint Networks." In Constraint Programming, 116–39. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-642-85983-0_5.

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2

Gottlob, Georg. "On Minimal Constraint Networks." In Principles and Practice of Constraint Programming – CP 2011, 325–39. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23786-7_26.

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3

Hamadi, Youssef. "Boosting Distributed Constraint Networks." In Combinatorial Search: From Algorithms to Systems, 5–26. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41482-4_2.

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Maggini, Marco, and Tiziano Papini. "Multitask Semi–supervised Learning with Constraints and Constraint Exceptions." In Artificial Neural Networks – ICANN 2010, 218–27. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15825-4_27.

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Xiang, Yang, and Wanling Zhang. "Multiagent Constraint Satisfaction with Multiply Sectioned Constraint Networks." In Advances in Artificial Intelligence, 228–40. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-72665-4_20.

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Day, John. "Learning by Constraint Relaxation." In Neural Networks and Soft Computing, 596–601. Heidelberg: Physica-Verlag HD, 2003. http://dx.doi.org/10.1007/978-3-7908-1902-1_91.

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Zavatteri, Matteo, and Luca Viganò. "Conditional Uncertainty in Constraint Networks." In Lecture Notes in Computer Science, 130–60. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-05453-3_7.

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Xu, Lin. "Reformulation of Temporal Constraint Networks." In Lecture Notes in Computer Science, 347. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45622-8_39.

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Monfroglio, Angelo. "Neural networks for constraint satisfaction." In Advances in Artificial Intelligence, 102–7. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/3-540-57292-9_48.

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D’Almeida, Dominique, Jean-François Condotta, Christophe Lecoutre, and Lakhdar Saïs. "Relaxation of Qualitative Constraint Networks." In Lecture Notes in Computer Science, 93–108. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-73580-9_10.

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Тези доповідей конференцій з теми "Constraint networks"

1

Latour, Anna Louise D., Behrouz Babaki, and Siegfried Nijssen. "Stochastic Constraint Propagation for Mining Probabilistic Networks." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/159.

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Анотація:
A number of data mining problems on probabilistic networks can be modeled as Stochastic Constraint Optimization and Satisfaction Problems, i.e., problems that involve objectives or constraints with a stochastic component. Earlier methods for solving these problems used Ordered Binary Decision Diagrams (OBDDs) to represent constraints on probability distributions, which were decomposed into sets of smaller constraints and solved by Constraint Programming (CP) or Mixed Integer Programming (MIP) solvers. For the specific case of monotonic distributions, we propose an alternative method: a new propagator for a global OBDD-based constraint. We show that this propagator is (sub-)linear in the size of the OBDD, and maintains domain consistency. We experimentally evaluate the effectiveness of this global constraint in comparison to existing decomposition-based approaches, and show how this propagator can be used in combination with another data mining specific constraint present in CP systems. As test cases we use problems from the data mining literature.
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2

Linlin Cao and Bao-Gang Hu. "Generalized constraint neural network regression model subject to equality function constraints." In 2015 International Joint Conference on Neural Networks (IJCNN). IEEE, 2015. http://dx.doi.org/10.1109/ijcnn.2015.7280507.

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3

Van Baelen, Quinten, and Peter Karsmakers. "Constraint Guided Gradient Descent: Guided Training with Inequality Constraints." In ESANN 2022 - European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Louvain-la-Neuve (Belgium): Ciaco - i6doc.com, 2022. http://dx.doi.org/10.14428/esann/2022.es2022-105.

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4

Zavatteri, Matteo, and Luca Viganò. "Constraint Networks Under Conditional Uncertainty." In 10th International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and Technology Publications, 2018. http://dx.doi.org/10.5220/0006553400410052.

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5

Devlin, David, and Barry O'Sullivan. "Preferential Attachment in Constraint Networks." In 2009 21st IEEE International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2009. http://dx.doi.org/10.1109/ictai.2009.91.

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6

Sioutis, Michael, Zhiguo Long, and Tomi Janhunen. "On Robustness in Qualitative Constraint Networks." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/251.

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We introduce and study a notion of robustness in Qualitative Constraint Networks (QCNs), which are typically used to represent and reason about abstract spatial and temporal information. In particular, given a QCN, we are interested in obtaining a robust qualitative solution, or, a robust scenario of it, which is a satisfiable scenario that has a higher perturbation tolerance than any other, or, in other words, a satisfiable scenario that has more chances than any other to remain valid after it is altered. This challenging problem requires to consider the entire set of satisfiable scenarios of a QCN, whose size is usually exponential in the number of constraints of that QCN; however, we present a first algorithm that is able to compute a robust scenario of a QCN using linear space in the number of constraints. Preliminary results with a dataset from the job-shop scheduling domain, and a standard one, show the interest of our approach and highlight the fact that not all solutions are created equal.
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Cao, Linlin, Ran He, and Bao-Gang Hu. "Locally imposing function for Generalized Constraint Neural Networks - A study on equality constraints." In 2016 International Joint Conference on Neural Networks (IJCNN). IEEE, 2016. http://dx.doi.org/10.1109/ijcnn.2016.7727830.

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Yang, Dan, Jiang Liu, Ran Zhang, and Tao Huang. "Multi-Constraint Virtual Network Embedding Algorithm For Satellite Networks." In GLOBECOM 2020 - 2020 IEEE Global Communications Conference. IEEE, 2020. http://dx.doi.org/10.1109/globecom42002.2020.9347993.

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Wang, Jue, Bo Li та Yinghong Cao. "Constrained Multi-objective Optimization Algorithm Based on ε Adaptive Weighted Constraint Violation". У The 7th International Conference on Computer Engineering and Networks. Trieste, Italy: Sissa Medialab, 2017. http://dx.doi.org/10.22323/1.299.0062.

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Fernando, Terrence, Prasad Wimalaratne, Kevin Tan, and Norman Murray. "Interactive Product Simulation Environment for Assessing Assembly and Maintainability Tasks." In ASME 1999 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 1999. http://dx.doi.org/10.1115/imece1999-0173.

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Анотація:
Abstract This paper presents the design and implementation of an interactive product simulation environment for supporting interactive assembly and maintenance tasks. The system architecture of the constraint-based virtual environment is based on the integration of components such as OpenGL Optimizer, Parasolid geometric kernel, a Constraint Engine, an Assembly Relationship Graph (ARG) and a task model. The approach presented in this paper is based on pure geometric constraints. Techniques such as automatic constraint recognition, constraint satisfaction, constraint management and constrained motion are employed to support interactive assembly operations and realistic behaviour of assembly parts. The user inputs are handled using a task model based on Augmented Transition Networks (ATN). The current system has been evaluated using two industrial case studies.
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Звіти організацій з теми "Constraint networks"

1

Pearl, Judea. Dynamic Constraint Networks. Fort Belvoir, VA: Defense Technical Information Center, February 1994. http://dx.doi.org/10.21236/ada278396.

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2

Li, D., W. Imajuku, and J. Han. General Network Element Constraint Encoding for GMPLS-Controlled Networks. Edited by G. Bernstein and Y. Lee. RFC Editor, June 2015. http://dx.doi.org/10.17487/rfc7579.

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3

Blower, David J. Using Constraint Satisfaction Networks to Study Aircrew Selection for Advanced Cockpits. Fort Belvoir, VA: Defense Technical Information Center, January 1992. http://dx.doi.org/10.21236/ada258151.

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4

Pearl, Judea. Dynamic Constraints Networks. Fort Belvoir, VA: Defense Technical Information Center, October 1989. http://dx.doi.org/10.21236/ada219778.

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5

Mai Phuong, Nguyen, Hanna North, Duong Minh Tuan, and Nguyen Manh Cuong. Assessment of women’s benefits and constraints in participating in agroforestry exemplar landscapes. World Agroforestry, 2021. http://dx.doi.org/10.5716/wp21015.pdf.

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Анотація:
Participating in the exemplar landscapes of the Developing and Promoting Market-Based Agroforestry and Forest Rehabilitation Options for Northwest Vietnam project has had positive impacts on ethnic women, such as increasing their networks and decision-making and public speaking skills. However, the rate of female farmers accessing and using project extension material or participating in project nurseries and applying agroforestry techniques was limited. This requires understanding of the real needs and interests grounded in the socio-cultural contexts of the ethnic groups living in the Northern Mountain Region in Viet Nam, who have unique social and cultural norms and values. The case studies show that agricultural activities are highly gendered: men and women play specific roles and have different, particular constraints and interests. Women are highly constrained by gender norms, access to resources, decision-making power and a prevailing positive-feedback loop of time poverty, especially in the Hmong community. A holistic, timesaving approach to addressing women’s daily activities could reduce the effects of time poverty and increase project participation. As women were highly willing to share project information, the project’s impacts would be more successful with increased participation by women through utilizing informal channels of communication and knowledge dissemination. Extension material designed for ethnic women should have less text and more visuals. Access to information is a critical constraint that perpetuates the norm that men are decision-makers, thereby, enhancing their perceived ownership, whereas women have limited access to information and so leave final decisions to men, especially in Hmong families. Older Hmong women have a Vietnamese (Kinh) language barrier, which further prevents them from accessing the project’s material. Further research into an adaptive framework that can be applied in a variety of contexts is recommended. This framework should prioritize time-saving activities for women and include material highlighting key considerations to maintain accountability among the project’s support staff.
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6

McDonnell, John R. Training Neural Networks with Weight Constraints. Fort Belvoir, VA: Defense Technical Information Center, March 1993. http://dx.doi.org/10.21236/ada264665.

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Bormann, C., M. Ersue, and A. Keranen. Terminology for Constrained-Node Networks. RFC Editor, May 2014. http://dx.doi.org/10.17487/rfc7228.

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Mahmoudi, Mona, and Guillermo Sapiro. Constrained Localization in Sensor Networks (Preprint). Fort Belvoir, VA: Defense Technical Information Center, October 2005. http://dx.doi.org/10.21236/ada516425.

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Schmitt, C., B. Stiller, and B. Trammell. TinyIPFIX for Smart Meters in Constrained Networks. RFC Editor, November 2017. http://dx.doi.org/10.17487/rfc8272.

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Bhandari, Vartika. Performance of Wireless Networks Subject to Constraints and Failures. Fort Belvoir, VA: Defense Technical Information Center, January 2008. http://dx.doi.org/10.21236/ada603092.

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