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Artículos de revistas sobre el tema "Constraint networks"

Autor: Grafiati

Publicado: 11 de marzo de 2023

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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 (2021): 6812–21. http://dx.doi.org/10.1609/aaai.v35i8.16841.

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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-

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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 (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

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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

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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 (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 constr

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5

Dechter, Rina, Itay Meiri, and Judea Pearl. "Temporal constraint networks." Artificial Intelligence 49, no. 1-3 (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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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 c

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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 tha

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8

Buscema, Massimo. "Constraint Satisfaction Neural Networks." Substance Use & Misuse 33, no. 2 (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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11

Gröflin, Heinz. "On node constraint networks." Networks 15, no. 4 (1985): 469–75. http://dx.doi.org/10.1002/net.3230150408.

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12

ABIDI, Bahae, Abdelillah JILBAB, and Mohamed EL HAZITI. "Security in wireless sensor networks." International Journal of Informatics and Communication Technology (IJ-ICT) 8, no. 1 (2019): 13. http://dx.doi.org/10.11591/ijict.v8i1.pp13-18.

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Even in difficult places to reach, the new networking technique allows the easy deployment of sensor networks, although these wireless sensor networks confront a lot of constraints. The major constraint is related to the quality of information sent by the network. The wireless sensor networks use different methods to achieve data to the base station. Data aggregation is an important one, used by these wireless sensor networks. But this aggregated data can be subject to several types of attacks and provides security is necessary to resist against malicious attacks, secure communication between

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13

Yang, Weijun, Xianxian Zeng, and Guanyu Lai. "A Guaranteed Approximation Algorithm for QoS Anypath Routing in WMNs." Mathematics 10, no. 23 (2022): 4557. http://dx.doi.org/10.3390/math10234557.

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Anypath routing is a hot research topic for QoS guarantee in wireless mesh networks (WMNs). According to time-varying characteristics of WMNs and the idea of anypath routing, a system network modeling method is proposed to address the multiple constrained optimization anypath problem. It focuses on the application of WMNs; under various QoS constraints, it satisfies a specific constraint and approaches other QoS constraints from an approximate perspective. A heuristic multi-constrained anypath algorithm with a time complexity as Dijkstra is proposed for the problem, and the algorithm is proved

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14

Hoernle, Nick, Rafael Michael Karampatsis, Vaishak Belle, and Kobi Gal. "MultiplexNet: Towards Fully Satisfied Logical Constraints in Neural Networks." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 5 (2022): 5700–5709. http://dx.doi.org/10.1609/aaai.v36i5.20512.

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We propose a novel way to incorporate expert knowledge into the training of deep neural networks. Many approaches encode domain constraints directly into the network architecture, requiring non-trivial or domain-specific engineering. In contrast, our approach, called MultiplexNet, represents domain knowledge as a quantifier-free logical formula in disjunctive normal form (DNF) which is easy to encode and to elicit from human experts. It introduces a latent Categorical variable that learns to choose which constraint term optimizes the error function of the network and it compiles the constraint

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15

Dunbar, R. I. M. "Structure and function in human and primate social networks: implications for diffusion, network stability and health." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 476, no. 2240 (2020): 20200446. http://dx.doi.org/10.1098/rspa.2020.0446.

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The human social world is orders of magnitude smaller than our highly urbanized world might lead us to suppose. In addition, human social networks have a very distinct fractal structure similar to that observed in other primates. In part, this reflects a cognitive constraint, and in part a time constraint, on the capacity for interaction. Structured networks of this kind have a significant effect on the rates of transmission of both disease and information. Because the cognitive mechanism underpinning network structure is based on trust, internal and external threats that undermine trust or co

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16

Xiang, Yang, Younis Mohamed, and Wanling Zhang. "Distributed constraint satisfaction with multiply sectioned constraint networks." International Journal of Information and Decision Sciences 6, no. 2 (2014): 127. http://dx.doi.org/10.1504/ijids.2014.061771.

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17

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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18

LALLOUET, ARNAUD, and ANDREI LEGTCHENKO. "BUILDING CONSISTENCIES FOR PARTIALLY DEFINED CONSTRAINTS WITH DECISION TREES AND NEURAL NETWORKS." International Journal on Artificial Intelligence Tools 16, no. 04 (2007): 683–706. http://dx.doi.org/10.1142/s0218213007003503.

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Partially Defined Constraints can be used to model the incomplete knowledge of a concept or a relation. Instead of only computing with the known part of the constraint, we propose to complete its definition by using Machine Learning techniques. Since constraints are actively used during solving for pruning domains, building a classifier for instances is not enough: we need a solver able to reduce variable domains. Our technique is composed of two steps: first we learn a classifier for each constraint projections and then we transform the classifiers into a propagator. The first contribution is

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19

Ma, Lei, and Dapeng Li. "Adaptive Neural Networks Control Using Barrier Lyapunov Functions for DC Motor System with Time-Varying State Constraints." Complexity 2018 (2018): 1–9. http://dx.doi.org/10.1155/2018/5082401.

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This paper proposes an adaptive neural network (NN) control approach for a direct-current (DC) system with full state constraints. To guarantee that state constraints always remain in the asymmetric time-varying constraint regions, the asymmetric time-varying Barrier Lyapunov Function (BLF) is employed to structure an adaptive NN controller. As we all know that the constant constraint is only a special case of the time-varying constraint, hence, the proposed control method is more general for dealing with constraint problem as compared with the existing works on DC systems. As far as we know,

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20

MONFROGLIO, ANGELO. "Neural Networks for Constraint Satisfaction." Connection Science 5, no. 2 (1993): 169–87. http://dx.doi.org/10.1080/09540099308915694.

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21

Li, Daqing, Guanliang Li, Kosmas Kosmidis, H. E. Stanley, Armin Bunde, and Shlomo Havlin. "Percolation of spatially constraint networks." EPL (Europhysics Letters) 93, no. 6 (2011): 68004. http://dx.doi.org/10.1209/0295-5075/93/68004.

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22

Monfroglio, Angelo. "Connectionist networks for constraint satisfaction." Neurocomputing 3, no. 1 (1991): 29–49. http://dx.doi.org/10.1016/0925-2312(91)90018-7.

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23

Dechter, Rina, and Judea Pearl. "Tree clustering for constraint networks." Artificial Intelligence 38, no. 3 (1989): 353–66. http://dx.doi.org/10.1016/0004-3702(89)90037-4.

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24

Meiri, Itay, Rina Dechter, and Judea Pearl. "Uncovering trees in constraint networks." Artificial Intelligence 86, no. 2 (1996): 245–67. http://dx.doi.org/10.1016/0004-3702(95)00102-6.

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25

Khandker, M. R., and B. Sultana. "Upper Bound on Blocking Probability for Vertically Stacked Optical Banyan Networks with Link Failures and Given Crosstalk Constraint." Journal of Scientific Research 1, no. 3 (2009): 484–94. http://dx.doi.org/10.3329/jsr.v1i3.1191.

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Vertical stacking of multiple copies of an optical banyan network is a novel scheme for building nonblocking optical switching networks. The resulting network, namely vertically stacked optical banyan (VSOB) network, preserves all the properties of the banyan network, but increases the hardware cost significantly under first order crosstalk-free constraint. However, stringent crosstalk constraint may not always be necessary. Considering the fact that some designer may want to compromise the blocking probability and crosstalk constraint to a certain degree with the hardware cost, blocking behav

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26

Xu, Ying, Kun Wang, Changhui Jiang, et al. "Motion-Constrained GNSS/INS Integrated Navigation Method Based on BP Neural Network." Remote Sensing 15, no. 1 (2022): 154. http://dx.doi.org/10.3390/rs15010154.

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The global navigation satellite system (GNSS) and inertial navigation system (INS) integrated navigation system have been widely used in Intelligent Transportation Systems (ITSs). However, the positioning error of integrated navigation systems is rapidly divergent when GNSS outages occur. Motion constraint and back propagation (BP) neural networks can provide additional knowledge to solve this issue. However, the predictions of a neural network have outliers and motion constraint is difficult to adapt according to the motion states of vehicles and boats. Therefore, this paper fused a BP neural

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27

Wen, Longfei, Lingguo Cui, Senchun Chai, and Baihai Zhang. "Neighbor Constraint Assisted Distributed Localization for Wireless Sensor Networks." Mathematical Problems in Engineering 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/143938.

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Localization is one of the most significant technologies in wireless sensor networks (WSNs) since it plays a critical role in many applications. The main idea in most localization methods is to estimate the sensor-anchor distances that are used by sensors to locate themselves. However, the distance information is always imprecise due to the measurement or estimation errors. In this work, a novel algorithm called neighbor constraint assisted distributed localization (NCA-DL) is proposed, which introduces the application of geometric constraints to these distances within the algorithm. For examp

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28

Chen, Yourong, Zhangquan Wang, Tiaojuan Ren, Yaolin Liu, and Hexin Lv. "Maximizing Lifetime of Wireless Sensor Networks with Mobile Sink Nodes." Mathematical Problems in Engineering 2014 (2014): 1–13. http://dx.doi.org/10.1155/2014/762979.

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In order to maximize network lifetime and balance energy consumption when sink nodes can move, maximizing lifetime of wireless sensor networks with mobile sink nodes (MLMS) is researched. The movement path selection method of sink nodes is proposed. Modified subtractive clustering method, k-means method, and nearest neighbor interpolation method are used to obtain the movement paths. The lifetime optimization model is established under flow constraint, energy consumption constraint, link transmission constraint, and other constraints. The model is solved from the perspective of static and mobi

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29

SUYKENS, J. A. K., and J. VANDEWALLE. "CHAOS SYNCHRONIZATION: A LAGRANGE PROGRAMMING NETWORK APPROACH." International Journal of Bifurcation and Chaos 10, no. 04 (2000): 797–810. http://dx.doi.org/10.1142/s0218127400000566.

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In this paper we interpret chaos synchronization schemes within the framework of Lagrange programming networks, which form a class of continuous-time optimization methods for solving constrained nonlinear optimization problems. From this study it follows that standard synchronization schemes can be regarded as a Lagrange programming network with soft constraining, where synchronization between state vectors is defined as a constraint to the dynamical systems. New schemes are proposed then which implement synchronization by hard and soft constraints within Lagrange programming networks. A versi

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30

LEE, J. H. M., and V. W. L. TAM. "A FRAMEWORK FOR INTEGRATING ARTIFICIAL NEURAL NETWORKS AND LOGIC PROGRAMMING." International Journal on Artificial Intelligence Tools 04, no. 01n02 (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

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31

Ho, Khuong Van, Son Que Vo, Tra Thanh Luu, and Lien Hong Pham. "On the performance of cooperative cognitive networks with selection combining and proactive relay selection." Science and Technology Development Journal 18, no. 3 (2015): 29–38. http://dx.doi.org/10.32508/stdj.v18i3.882.

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This paper proposes an outage analysis framework for cooperative cognitive networks with proactive relay selection and selection combining (SC) under licensed outage constraint, maximum transmit power constraint, independent non-identical (i.n.i) fading distributions, erroneous channel information, and licensed users’ interference. Towards this end, we firstly suggest power allocation for unlicensed transmitters to satisfy power constraints and account for erroneous channel information and licensed users’ interference. Then, we propose an exact closed-form outage probability formula for the un

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32

ZUO, JING, XUEFEN CHI, LIN GUAN, HONGXIA LI, and IRFAN AWAN. "DESIGN OF FUZZY BASED MULTI-CONSTRAINED ROUTING PROTOCOL AND THE PERFORMANCE EVALUATION." Journal of Interconnection Networks 09, no. 04 (2008): 369–87. http://dx.doi.org/10.1142/s0219265908002333.

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Single-constrained QoS routing protocols have inherent defects when applied into wireless ad hoc networks. Due to a single constraint parameter is only considered, they can't always cope with the problems caused by the uncertainty of ad hoc networks well. They are not robust enough. In order to overcome the drawbacks of single-constrained QoS routing protocols and improve the Quality of Service (QoS) of ad hoc networks, this paper proposed a multi-constrained QoS routing protocol based on fuzzy logic. It is developed from Dynamic Source Routing (DSR). The proposed protocol is service-aware in

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33

Zhang, Yu, and Zhiming Hu. "Parameter Learning of Bayesian Network with Multiplicative Synergistic Constraints." Symmetry 14, no. 7 (2022): 1469. http://dx.doi.org/10.3390/sym14071469.

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Learning the conditional probability table (CPT) parameters of Bayesian networks (BNs) is a key challenge in real-world decision support applications, especially when there are limited data available. The traditional approach to this challenge is introducing domain knowledge/expert judgments that are encoded as qualitative parameter constraints. In this paper, we focus on multiplicative synergistic constraints. The negative multiplicative synergy constraint and positive multiplicative synergy constraint in this paper are symmetric. In order to integrate multiplicative synergistic constraints i

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34

Ay, Nihat. "Locality of Global Stochastic Interaction in Directed Acyclic Networks." Neural Computation 14, no. 12 (2002): 2959–80. http://dx.doi.org/10.1162/089976602760805368.

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The hypothesis of invariant maximization of interaction (IMI) is formulated within the setting of random fields. According to this hypothesis, learning processes maximize the stochastic interaction of the neurons subject to constraints. We consider the extrinsic constraint in terms of a fixed input distribution on the periphery of the network. Our main intrinsic constraint is given by a directed acyclic network structure. First mathematical results about the strong relation of the local information flow and the global interaction are stated in order to investigate the possibility of controllin

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35

Liu, Dongsheng, Sai Zhao, Quanzhong Li, and Jiayin Qin. "Rate Maximization for Suspicious Multicast Communication Networks with Full-Duplex Proactive Monitoring." Mobile Information Systems 2020 (July 10, 2020): 1–6. http://dx.doi.org/10.1155/2020/7847623.

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In this paper, we investigate the optimization of the monitoring rate for a suspicious multicast communication network with a legitimate full-duplex (FD) monitor, where the FD monitor is proactive to jam suspicious receivers and eavesdrop from the suspicious transmitter simultaneously. To effectively monitor the suspicious communication over multicast networks, we maximize the monitoring rate under the outage probability constraint of the suspicious multicast communication network and the jamming power constraint at the FD monitor. The formulated optimization problem is nonconvex, and its glob

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36

Watts, J. W. W., G. C. C. Fleming, and Q. Lu. "Determination of Active Constraints in a Network." SPE Journal 17, no. 02 (2012): 441–54. http://dx.doi.org/10.2118/118877-pa.

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Summary There is increasing interest in modeling networks of wells, including subsurface components of complex wells and surface facilities. Such modeling requires setting constraints at various points in the network. Typical constraints are maximum phase flow rates and minimum flowing pressures. A major difficulty in network calculations is determining which of these constraints is active. This paper presents a method that uses slack variables in determining active constraints. The linearized equations of interest generally come in pairs, with each pair consisting of a base equation and a con

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37

Yang, Liu, Hongbin Zhu, Haifeng Wang, Kai Kang, and Hua Qian. "Data censoring with network lifetime constraint in wireless sensor networks." Digital Signal Processing 92 (September 2019): 73–81. http://dx.doi.org/10.1016/j.dsp.2019.05.004.

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38

Liu, Dong, Yan Ru, Qinpeng Li, Shibin Wang, and Jianwei Niu. "Semisupervised Community Preserving Network Embedding with Pairwise Constraints." Complexity 2020 (November 10, 2020): 1–14. http://dx.doi.org/10.1155/2020/7953758.

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Network embedding aims to learn the low-dimensional representations of nodes in networks. It preserves the structure and internal attributes of the networks while representing nodes as low-dimensional dense real-valued vectors. These vectors are used as inputs of machine learning algorithms for network analysis tasks such as node clustering, classification, link prediction, and network visualization. The network embedding algorithms, which considered the community structure, impose a higher level of constraint on the similarity of nodes, and they make the learned node embedding results more di

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39

Davies, T. H. "Freedom and Constraint in Coupling Networks." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 220, no. 7 (2006): 989–1010. http://dx.doi.org/10.1243/09544062c09105.

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Kirchhoff's circulation law has previously been adapted for studies of under-constraint. Here Kirchhoff's cutset law is also adapted for studies of overconstraint. The two procedures are presented in tandem to emphasise their duality. The same tasks are then undertaken using dual virtual power methods. One is a new procedure for underconstraint that introduces the concepts of cutset motion and virtual action, dual with previously reported circuit action and virtual motion. Of the four methods described, the superior ones for each task are identified.

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40

Guangleng Xiong and Tao Li. "Robust design based on constraint networks." IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans 32, no. 5 (2002): 596–604. http://dx.doi.org/10.1109/tsmca.2002.804814.

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41

Kruschke, John K. "State transitions in constraint satisfaction networks." Behavioral and Brain Sciences 12, no. 3 (1989): 407–8. http://dx.doi.org/10.1017/s0140525x00056910.

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42

Bettini, Claudio, X. Sean Wang, and Sushil Jajodia. "Solving multi-granularity temporal constraint networks." Artificial Intelligence 140, no. 1-2 (2002): 107–52. http://dx.doi.org/10.1016/s0004-3702(02)00223-0.

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43

Schwalb, Eddie, and Rina Dechter. "Processing disjunctions in temporal constraint networks." Artificial Intelligence 93, no. 1-2 (1997): 29–61. http://dx.doi.org/10.1016/s0004-3702(97)00009-x.

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44

Marín, R., M. A. Cárdenas, M. Balsa, and J. L. Sanchez. "Obtaining solutions in fuzzy constraint networks." International Journal of Approximate Reasoning 16, no. 3-4 (1997): 261–88. http://dx.doi.org/10.1016/s0888-613x(96)00125-9.

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45

Monfroglio, Angelo. "Backpropagation networks for logic constraint solving." Neurocomputing 6, no. 1 (1994): 67–98. http://dx.doi.org/10.1016/0925-2312(94)90035-3.

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46

Monfroglio, Angelo. "Neural networks for finite constraint satisfaction." Neural Computing & Applications 3, no. 2 (1995): 78–100. http://dx.doi.org/10.1007/bf01421960.

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47

Condotta, Jean-François, Souhila Kaci, and Yakoub Salhi. "Optimization in temporal qualitative constraint networks." Acta Informatica 53, no. 2 (2015): 149–70. http://dx.doi.org/10.1007/s00236-015-0228-z.

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48

Meisels, Amnon, Jihad Ell-sana, and Ehud Gudes. "Decomposing and Solving Timetabling Constraint Networks." Computational Intelligence 13, no. 4 (1997): 486–505. http://dx.doi.org/10.1111/0824-7935.00049.

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49

HAMADI, YOUSSEF. "INTERLEAVED BACKTRACKING IN DISTRIBUTED CONSTRAINT NETWORKS." International Journal on Artificial Intelligence Tools 11, no. 02 (2002): 167–88. http://dx.doi.org/10.1142/s0218213002000836.

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The adaptation of software technology to distributed environments is an important challenge today. In this work we combine parallel and distributed search. By this way we add the potential speed-up of a parallel exploration in the processing of distributed problems. This paper extends DIBT, a distributed search procedure operating in distributed constraint networks.11 The extension is threefold. First, the ordered hierarchies used during backtracking are extended to remove partial orders. Second the procedure is updated to face delayed information problems upcoming in heterogeneous (We would l

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Hassani Bijarbooneh, Farshid. "Constraint programming for wireless sensor networks." Constraints 20, no. 4 (2015): 508. http://dx.doi.org/10.1007/s10601-015-9228-4.

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