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Artículos de revistas sobre el tema "Learning from Constraints"

Autor: Grafiati

Publicado: 10 de marzo de 2023

Última modificación: 14 de marzo de 2023

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1

Cropper, Andrew, and Rolf Morel. "Learning programs by learning from failures." Machine Learning 110, no. 4 (2021): 801–56. http://dx.doi.org/10.1007/s10994-020-05934-z.

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AbstractWe describe an inductive logic programming (ILP) approach called learning from failures. In this approach, an ILP system (the learner) decomposes the learning problem into three separate stages: generate, test, and constrain. In the generate stage, the learner generates a hypothesis (a logic program) that satisfies a set of hypothesis constraints (constraints on the syntactic form of hypotheses). In the test stage, the learner tests the hypothesis against training examples. A hypothesis fails when it does not entail all the positive examples or entails a negative example. If a hypothes

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2

Chou, Glen, Dmitry Berenson, and Necmiye Ozay. "Learning constraints from demonstrations with grid and parametric representations." International Journal of Robotics Research 40, no. 10-11 (2021): 1255–83. http://dx.doi.org/10.1177/02783649211035177.

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We extend the learning from demonstration paradigm by providing a method for learning unknown constraints shared across tasks, using demonstrations of the tasks, their cost functions, and knowledge of the system dynamics and control constraints. Given safe demonstrations, our method uses hit-and-run sampling to obtain lower cost, and thus unsafe, trajectories. Both safe and unsafe trajectories are used to obtain a consistent representation of the unsafe set via solving an integer program. Our method generalizes across system dynamics and learns a guaranteed subset of the constraint. In additio

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3

Okabe, Masayuki, and Seiji Yamada. "Learning Similarity Matrix from Constraints of Relational Neighbors." Journal of Advanced Computational Intelligence and Intelligent Informatics 14, no. 4 (2010): 402–7. http://dx.doi.org/10.20965/jaciii.2010.p0402.

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This paper describes a method of learning similarity matrix from pairwise constraints assumed used under the situation such as interactive clustering, where we can expect little user feedback. With the small number of pairwise constraints used, our method attempts to use additional constraints induced by the affinity relationship between constrained data and their neighbors. The similarity matrix is learned by solving an optimization problem formalized as semidefinite programming. Additional constraints are used as complementary in the optimization problem. Results of experiments confirmed the

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4

Mueller, Carl L. "Abstract Constraints for Safe and Robust Robot Learning from Demonstration." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 10 (2020): 13728–29. http://dx.doi.org/10.1609/aaai.v34i10.7136.

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My thesis research incorporates high-level abstract behavioral requirements, called ‘conceptual constraints’, into the modeling processes of robot Learning from Demonstration (LfD) techniques. My most recent work introduces an LfD algorithm called Concept Constrained Learning from Demonstration. This algorithm encodes motion planning constraints as temporal Boolean operators that enforce high-level constraints over portions of the robot's motion plan during learned skill execution. This results in more easily trained, more robust, and safer learned skills. Future work will incorporate conceptu

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5

Kato, Tsuyoshi, Wataru Fujibuchi, and Kiyoshi Asai. "Learning Kernels from Distance Constraints." IPSJ Digital Courier 2 (2006): 441–51. http://dx.doi.org/10.2197/ipsjdc.2.441.

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6

Farina, Francesco, Stefano Melacci, Andrea Garulli, and Antonio Giannitrapani. "Asynchronous Distributed Learning From Constraints." IEEE Transactions on Neural Networks and Learning Systems 31, no. 10 (2020): 4367–73. http://dx.doi.org/10.1109/tnnls.2019.2947740.

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7

Hammer, Rubi, Tomer Hertz, Shaul Hochstein, and Daphna Weinshall. "Category learning from equivalence constraints." Cognitive Processing 10, no. 3 (2008): 211–32. http://dx.doi.org/10.1007/s10339-008-0243-x.

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8

Armesto, Leopoldo, João Moura, Vladimir Ivan, Mustafa Suphi Erden, Antonio Sala, and Sethu Vijayakumar. "Constraint-aware learning of policies by demonstration." International Journal of Robotics Research 37, no. 13-14 (2018): 1673–89. http://dx.doi.org/10.1177/0278364918784354.

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Many practical tasks in robotic systems, such as cleaning windows, writing, or grasping, are inherently constrained. Learning policies subject to constraints is a challenging problem. In this paper, we propose a method of constraint-aware learning that solves the policy learning problem using redundant robots that execute a policy that is acting in the null space of a constraint. In particular, we are interested in generalizing learned null-space policies across constraints that were not known during the training. We split the combined problem of learning constraints and policies into two: fir

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9

Hewing, Lukas, Kim P. Wabersich, Marcel Menner, and Melanie N. Zeilinger. "Learning-Based Model Predictive Control: Toward Safe Learning in Control." Annual Review of Control, Robotics, and Autonomous Systems 3, no. 1 (2020): 269–96. http://dx.doi.org/10.1146/annurev-control-090419-075625.

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Recent successes in the field of machine learning, as well as the availability of increased sensing and computational capabilities in modern control systems, have led to a growing interest in learning and data-driven control techniques. Model predictive control (MPC), as the prime methodology for constrained control, offers a significant opportunity to exploit the abundance of data in a reliable manner, particularly while taking safety constraints into account. This review aims at summarizing and categorizing previous research on learning-based MPC, i.e., the integration or combination of MPC

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10

Wu, Xintao, and Daniel Barbará. "Learning missing values from summary constraints." ACM SIGKDD Explorations Newsletter 4, no. 1 (2002): 21–30. http://dx.doi.org/10.1145/568574.568579.

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11

Ren, Hongyu, Russell Stewart, Jiaming Song, Volodymyr Kuleshov, and Stefano Ermon. "Learning with Weak Supervision from Physics and Data-Driven Constraints." AI Magazine 39, no. 1 (2018): 27–38. http://dx.doi.org/10.1609/aimag.v39i1.2776.

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In many applications of machine learning, labeled data is scarce and obtaining additional labels is expensive. We introduce a new approach to supervising learning algorithms without labels by enforcing a small number of domain-specific constraints over the algorithms’ outputs. The constraints can be provided explicitly based on prior knowledge — e.g. we may require that objects detected in videos satisfy the laws of physics — or implicitly extracted from data using a novel framework inspired by adversarial training. We demonstrate the effectiveness of constraint-based learning on a variety of

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12

Xu, Haoran, Xianyuan Zhan, and Xiangyu Zhu. "Constraints Penalized Q-learning for Safe Offline Reinforcement Learning." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 8 (2022): 8753–60. http://dx.doi.org/10.1609/aaai.v36i8.20855.

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We study the problem of safe offline reinforcement learning (RL), the goal is to learn a policy that maximizes long-term reward while satisfying safety constraints given only offline data, without further interaction with the environment. This problem is more appealing for real world RL applications, in which data collection is costly or dangerous. Enforcing constraint satisfaction is non-trivial, especially in offline settings, as there is a potential large discrepancy between the policy distribution and the data distribution, causing errors in estimating the value of safety constraints. We s

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13

Onishi, K. "Learning phonotactic constraints from brief auditory experience." Cognition 83, no. 1 (2002): B13—B23. http://dx.doi.org/10.1016/s0010-0277(01)00165-2.

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14

Suraweera, Pramuditha, Geoffrey I. Webb, Ian Evans, and Mark Wallace. "Learning crew scheduling constraints from historical schedules." Transportation Research Part C: Emerging Technologies 26 (January 2013): 214–32. http://dx.doi.org/10.1016/j.trc.2012.08.002.

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15

Moon, In-Ho, and Kevin Harer. "Learning from Constraints for Formal Property Checking." Journal of Electronic Testing 26, no. 2 (2010): 243–59. http://dx.doi.org/10.1007/s10836-010-5143-1.

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16

Ciravegna, Gabriele, Francesco Giannini, Stefano Melacci, Marco Maggini, and Marco Gori. "A Constraint-Based Approach to Learning and Explanation." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 3658–65. http://dx.doi.org/10.1609/aaai.v34i04.5774.

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In the last few years we have seen a remarkable progress from the cultivation of the idea of expressing domain knowledge by the mathematical notion of constraint. However, the progress has mostly involved the process of providing consistent solutions with a given set of constraints, whereas learning “new” constraints, that express new knowledge, is still an open challenge. In this paper we propose a novel approach to learning of constraints which is based on information theoretic principles. The basic idea consists in maximizing the transfer of information between task functions and a set of l

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17

Guo, Yufan, Roi Reichart, and Anna Korhonen. "Unsupervised Declarative Knowledge Induction for Constraint-Based Learning of Information Structure in Scientific Documents." Transactions of the Association for Computational Linguistics 3 (December 2015): 131–43. http://dx.doi.org/10.1162/tacl_a_00128.

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Inferring the information structure of scientific documents is useful for many NLP applications. Existing approaches to this task require substantial human effort. We propose a framework for constraint learning that reduces human involvement considerably. Our model uses topic models to identify latent topics and their key linguistic features in input documents, induces constraints from this information and maps sentences to their dominant information structure categories through a constrained unsupervised model. When the induced constraints are combined with a fully unsupervised model, the res

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18

Alderete, John, Paul Tupper, and Stefan A. Frisch. "Phonological constraint induction in a connectionist network: learning OCP-Place constraints from data." Language Sciences 37 (May 2013): 52–69. http://dx.doi.org/10.1016/j.langsci.2012.10.002.

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19

Ahmed, Kareem, Tao Li, Thy Ton, et al. "PYLON: A PyTorch Framework for Learning with Constraints." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 11 (2022): 13152–54. http://dx.doi.org/10.1609/aaai.v36i11.21711.

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Deep learning excels at learning task information from large amounts of data, but struggles with learning from declarative high-level knowledge that can be more succinctly expressed directly. In this work, we introduce PYLON, a neuro-symbolic training framework that builds on PyTorch to augment procedurally trained models with declaratively specified knowledge. PYLON lets users programmatically specify constraints as Python functions and compiles them into a differentiable loss, thus training predictive models that fit the data whilst satisfying the specified constraints. PYLON includes both e

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20

Gnecco, Giorgio, Marco Gori, Stefano Melacci, and Marcello Sanguineti. "Foundations of Support Constraint Machines." Neural Computation 27, no. 2 (2015): 388–480. http://dx.doi.org/10.1162/neco_a_00686.

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The mathematical foundations of a new theory for the design of intelligent agents are presented. The proposed learning paradigm is centered around the concept of constraint, representing the interactions with the environment, and the parsimony principle. The classical regularization framework of kernel machines is naturally extended to the case in which the agents interact with a richer environment, where abstract granules of knowledge, compactly described by different linguistic formalisms, can be translated into the unified notion of constraint for defining the hypothesis set. Constrained va

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21

Ji, Chuanyi, Robert R. Snapp, and Demetri Psaltis. "Generalizing Smoothness Constraints from Discrete Samples." Neural Computation 2, no. 2 (1990): 188–97. http://dx.doi.org/10.1162/neco.1990.2.2.188.

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We study how certain smoothness constraints, for example, piecewise continuity, can be generalized from a discrete set of analog-valued data, by modifying the error backpropagation, learning algorithm. Numerical simulations demonstrate that by imposing two heuristic objectives — (1) reducing the number of hidden units, and (2) minimizing the magnitudes of the weights in the network — during the learning process, one obtains a network with a response function that smoothly interpolates between the training data.

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22

Howard, Matthew, Stefan Klanke, Michael Gienger, Christian Goerick, and Sethu Vijayakumar. "Behaviour Generation in Humanoids by Learning Potential-Based Policies from Constrained Motion." Applied Bionics and Biomechanics 5, no. 4 (2008): 195–211. http://dx.doi.org/10.1155/2008/316371.

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Movement generation that is consistent with observed or demonstrated behaviour is an efficient way to seed movement planning in complex, high-dimensional movement systems like humanoid robots. We present a method for learning potential-based policies from constrained motion data. In contrast to previous approaches to direct policy learning, our method can combine observations from a variety of contexts where different constraints are in force, to learn the underlying unconstrained policy in form of its potential function. This allows us to generalise and predict behaviour where novel constrain

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23

Maggini, Marco, Stefano Melacci, and Lorenzo Sarti. "Learning from pairwise constraints by Similarity Neural Networks." Neural Networks 26 (February 2012): 141–58. http://dx.doi.org/10.1016/j.neunet.2011.10.009.

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24

Hüllermeier, Eyke. "Flexible constraints for regularization in learning from data." International Journal of Intelligent Systems 19, no. 6 (2004): 525–41. http://dx.doi.org/10.1002/int.20010.

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25

Yang, Qisong, Thiago D. Simão, Simon H. Tindemans, and Matthijs T. J. Spaan. "WCSAC: Worst-Case Soft Actor Critic for Safety-Constrained Reinforcement Learning." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 12 (2021): 10639–46. http://dx.doi.org/10.1609/aaai.v35i12.17272.

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Safe exploration is regarded as a key priority area for reinforcement learning research. With separate reward and safety signals, it is natural to cast it as constrained reinforcement learning, where expected long-term costs of policies are constrained. However, it can be hazardous to set constraints on the expected safety signal without considering the tail of the distribution. For instance, in safety-critical domains, worst-case analysis is required to avoid disastrous results. We present a novel reinforcement learning algorithm called Worst-Case Soft Actor Critic, which extends the Soft Act

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26

Smith, Jennifer L. "From experiment results to a constraint hierarchy with the ‘Rank Centrality’ algorithm." Proceedings of the Linguistic Society of America 5, no. 1 (2020): 144. http://dx.doi.org/10.3765/plsa.v51.4694.

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Rank Centrality (RC; Negahban, Oh, & Shah 2017) is a rank-aggregation algorithm that computes a total ranking of elements from noisy pairwise ranking information. I test RC as an alternative to incremental error-driven learning algorithms such as GLA-MaxEnt (Boersma & Hayes 2001; Jäger 2007) for modeling a constraint hierarchy on the basis of two-alternative forced-choice experiment results. For the case study examined here, RC agrees well with GLA-MaxEnt on the ordering of the constraints, but differs somewhat on the distance between constraints; in particular, RC assigns more extreme

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27

Smith, Jennifer L. "From experiment results to a constraint hierarchy with the ‘Rank Centrality’ algorithm." Proceedings of the Linguistic Society of America 5, no. 1 (2020): 144. http://dx.doi.org/10.3765/plsa.v5i1.4694.

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Rank Centrality (RC; Negahban, Oh, & Shah 2017) is a rank-aggregation algorithm that computes a total ranking of elements from noisy pairwise ranking information. I test RC as an alternative to incremental error-driven learning algorithms such as GLA-MaxEnt (Boersma & Hayes 2001; Jäger 2007) for modeling a constraint hierarchy on the basis of two-alternative forced-choice experiment results. For the case study examined here, RC agrees well with GLA-MaxEnt on the ordering of the constraints, but differs somewhat on the distance between constraints; in particular, RC assigns more extreme

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28

Diallo, Aïssatou, and Johannes Fürnkranz. "Learning Ordinal Embedding from Sets." Entropy 23, no. 8 (2021): 964. http://dx.doi.org/10.3390/e23080964.

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Ordinal embedding is the task of computing a meaningful multidimensional representation of objects, for which only qualitative constraints on their distance functions are known. In particular, we consider comparisons of the form “Which object from the pair (j,k) is more similar to object i?”. In this paper, we generalize this framework to the case where the ordinal constraints are not given at the level of individual points, but at the level of sets, and propose a distributional triplet embedding approach in a scalable learning framework. We show that the query complexity of our approach is on

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29

Wah, B. W. "Population-based learning: a method for learning from examples under resource constraints." IEEE Transactions on Knowledge and Data Engineering 4, no. 5 (1992): 454–74. http://dx.doi.org/10.1109/69.166988.

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30

NEUMANN, KLAUS, MATTHIAS ROLF, and JOCHEN JAKOB STEIL. "RELIABLE INTEGRATION OF CONTINUOUS CONSTRAINTS INTO EXTREME LEARNING MACHINES." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 21, supp02 (2013): 35–50. http://dx.doi.org/10.1142/s021848851340014x.

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The application of machine learning methods in the engineering of intelligent technical systems often requires the integration of continuous constraints like positivity, monotonicity, or bounded curvature in the learned function to guarantee a reliable performance. We show that the extreme learning machine is particularly well suited for this task. Constraints involving arbitrary derivatives of the learned function are effectively implemented through quadratic optimization because the learned function is linear in its parameters, and derivatives can be derived analytically. We further provide

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31

Warker, Jill A., Gary S. Dell, Christine A. Whalen, and Samantha Gereg. "Limits on learning phonotactic constraints from recent production experience." Journal of Experimental Psychology: Learning, Memory, and Cognition 34, no. 5 (2008): 1289–95. http://dx.doi.org/10.1037/a0013033.

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32

O'Toole, Alice J. "Structure from Stereo by Associative Learning of the Constraints." Perception 18, no. 6 (1989): 767–82. http://dx.doi.org/10.1068/p180767.

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A computational model of structure from stereo that develops smoothness constraints naturally by associative learning of a large number of example mappings from disparity data to surface depth data is proposed. Banks of disparity-selective graded response units at all spatial locations in the visual field were the input data. These cells responded to matches of luminance change at convergent, divergent, or zero offsets in the left and right ‘retina’ samples. Surfaces were created by means of a pseudo-Markov process. From these surfaces, shaded marked and ummarked surfaces were created, along w

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33

Gao, Shan, Chen Zu, and Daoqiang Zhang. "Learning mid-perpendicular hyperplane similarity from cannot-link constraints." Neurocomputing 113 (August 2013): 195–203. http://dx.doi.org/10.1016/j.neucom.2013.01.002.

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34

Egilmez, Hilmi E., Eduardo Pavez, and Antonio Ortega. "Graph Learning From Data Under Laplacian and Structural Constraints." IEEE Journal of Selected Topics in Signal Processing 11, no. 6 (2017): 825–41. http://dx.doi.org/10.1109/jstsp.2017.2726975.

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35

Puchkov, N. P. "Digital Didactics under Distance Learning Constraints." Voprosy sovremennoj nauki i praktiki. Universitet imeni V.I. Vernadskogo, no. 4(82) (2021): 154–64. http://dx.doi.org/10.17277/voprosy.2021.04.pp.154-164.

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The article considers methodological approaches to the process of eliminating the problems of digitalization of education using the example of the academic disciplines of mathematics and computer science. It is shown that the use of specially designed complex mathematical tasks provides a harmonious combination of analytical research inherent in classical mathematics and constantly progressing methods of numerical analysis and computer modeling. The substantive filling of educational tasks with elements of production situations from the future profession of students or from the process of thei

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36

Hayes, Bruce, and Colin Wilson. "A Maximum Entropy Model of Phonotactics and Phonotactic Learning." Linguistic Inquiry 39, no. 3 (2008): 379–440. http://dx.doi.org/10.1162/ling.2008.39.3.379.

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The study of phonotactics is a central topic in phonology. We propose a theory of phonotactic grammars and a learning algorithm that constructs such grammars from positive evidence. Our grammars consist of constraints that are assigned numerical weights according to the principle of maximum entropy. The grammars assess possible words on the basis of the weighted sum of their constraint violations. The learning algorithm yields grammars that can capture both categorical and gradient phonotactic patterns. The algorithm is not provided with constraints in advance, but uses its own resources to fo

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37

Qin, Xingli, Lingli Zhao, Jie Yang, et al. "Active Pairwise Constraint Learning in Constrained Time-Series Clustering for Crop Mapping from Airborne SAR Imagery." Remote Sensing 14, no. 23 (2022): 6073. http://dx.doi.org/10.3390/rs14236073.

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Airborne SAR is an important data source for crop mapping and has important applications in agricultural monitoring and food safety. However, the incidence-angle effects of airborne SAR imagery decrease the crop mapping accuracy. An active pairwise constraint learning method (APCL) is proposed for constrained time-series clustering to address this problem. APCL constructs two types of instance-level pairwise constraints based on the incidence angles of the samples and a non-iterative batch-mode active selection scheme: the must-link constraint, which links two objects of the same crop type wit

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38

Burness, Phillip, and Kevin McMullin. "Post-nasal voicing in Japanese classifiers as exceptional triggering: implications for Indexed Constraint Theory." Canadian Journal of Linguistics/Revue canadienne de linguistique 65, no. 4 (2020): 471–95. http://dx.doi.org/10.1017/cnj.2020.26.

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AbstractIndexed constraints are often used in constraint-based phonological frameworks to account for exceptions to generalizations. A point of contention in the literature on constraint indexation revolves around indexed markedness constraints. While some researchers argue that only faithfulness constraints should be indexed, others argue that markedness constraints should be eligible for indexation as well. This article presents data from Japanese for which a complete synchronic analysis requires indexed markedness constraints but argues that such constraints are only necessary in cases wher

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39

O'Sullivan, Barry. "Automated Modelling and Solving in Constraint Programming." Proceedings of the AAAI Conference on Artificial Intelligence 24, no. 1 (2010): 1493–97. http://dx.doi.org/10.1609/aaai.v24i1.7530.

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Constraint programming can be divided very crudely into modeling and solving. Modeling defines the problem, in terms of variables that can take on different values, subject to restrictions (constraints) on which combinations of variables are allowed. Solving finds values for all the variables that simultaneously satisfy all the constraints. However, the impact of constraint programming has been constrained by a lack of "user-friendliness''. Constraint programming has a major "declarative" aspect, in that a problem model can be handed off for solution to a variety of standard solving methods. T

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40

Ma, Yecheng Jason, Andrew Shen, Osbert Bastani, and Jayaraman Dinesh. "Conservative and Adaptive Penalty for Model-Based Safe Reinforcement Learning." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 5 (2022): 5404–12. http://dx.doi.org/10.1609/aaai.v36i5.20478.

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Reinforcement Learning (RL) agents in the real world must satisfy safety constraints in addition to maximizing a reward objective. Model-based RL algorithms hold promise for reducing unsafe real-world actions: they may synthesize policies that obey all constraints using simulated samples from a learned model. However, imperfect models can result in real-world constraint violations even for actions that are predicted to satisfy all constraints. We propose Conservative and Adaptive Penalty (CAP), a model-based safe RL framework that accounts for potential modeling errors by capturing model uncer

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41

Bai, Wenjun, Changqin Quan, and Zhi-Wei Luo. "Improving Generative and Discriminative Modelling Performance by Implementing Learning Constraints in Encapsulated Variational Autoencoders." Applied Sciences 9, no. 12 (2019): 2551. http://dx.doi.org/10.3390/app9122551.

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Learning latent representations of observed data that can favour both discriminative and generative tasks remains a challenging task in artificial-intelligence (AI) research. Previous attempts that ranged from the convex binding of discriminative and generative models to the semisupervised learning paradigm could hardly yield optimal performance on both generative and discriminative tasks. To this end, in this research, we harness the power of two neuroscience-inspired learning constraints, that is, dependence minimisation and regularisation constraints, to improve generative and discriminativ

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42

DODARO, CARMINE, THOMAS EITER, PAUL OGRIS, and KONSTANTIN SCHEKOTIHIN. "Managing caching strategies for stream reasoning with reinforcement learning." Theory and Practice of Logic Programming 20, no. 5 (2020): 625–40. http://dx.doi.org/10.1017/s147106842000037x.

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AbstractEfficient decision-making over continuously changing data is essential for many application domains such as cyber-physical systems, industry digitalization, etc. Modern stream reasoning frameworks allow one to model and solve various real-world problems using incremental and continuous evaluation of programs as new data arrives in the stream. Applied techniques use, e.g., Datalog-like materialization or truth maintenance algorithms to avoid costly re-computations, thus ensuring low latency and high throughput of a stream reasoner. However, the expressiveness of existing approaches is q

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43

Hong, Junyuan, Haotao Wang, Zhangyang Wang, and Jiayu Zhou. "Learning Model-Based Privacy Protection under Budget Constraints." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 9 (2021): 7702–10. http://dx.doi.org/10.1609/aaai.v35i9.16941.

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Protecting privacy in gradient-based learning has become increasingly critical as more sensitive information is being used. Many existing solutions seek to protect the sensitive gradients by constraining the overall privacy cost within a constant budget, where the protection is hand-designed and empirically calibrated to boost the utility of the resulting model. However, it remains challenging to choose the proper protection adapted for specific constraints so that the utility is maximized. To this end, we propose a novel Learning-to-Protect algorithm that automatically learns a model-based pr

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44

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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Abed Alabaddi, Zaid Ahmad, Arwa Hisham Rahahleh, and Majd Mohammad Al-Omoush. "Blended E-Learning Constraints from the Viewpoint of Faculty Members." International Journal of Business and Management 11, no. 7 (2016): 180. http://dx.doi.org/10.5539/ijbm.v11n7p180.

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<p>This research aims to identify obstacles ‎ to the use of blended e-learning in Al-Balqa Applied University through the viewpoint of faculty members. This research also aims at finding out the constraints that this type of e-learning and finding appropriate solutions for these constraints in the future. The results of this research will also offer proposals and recommendations that will increase the effectiveness of this type of e-learning. Furthermore, the research also aims to find out the best method of training faculty members on how to use blended e-learning.</p><p>The

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Chou, Glen, Necmiye Ozay, and Dmitry Berenson. "Learning Constraints From Locally-Optimal Demonstrations Under Cost Function Uncertainty." IEEE Robotics and Automation Letters 5, no. 2 (2020): 3682–90. http://dx.doi.org/10.1109/lra.2020.2974427.

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Salih, Majid Mohammed, Usra Ahmed Jarjis, and Nidal Ali Suleiman. "E-learning, application constraints and remedies." Journal of University of Human Development 2, no. 4 (2016): 290. http://dx.doi.org/10.21928/juhd.v2n4y2016.pp290-317.

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We seek through the current study to take up the subject of a great deal of importance and the growing popularity in recent years by many of the students and educated as an opportunity for many individuals for the purpose of obtaining scientific certificates Aubramj training conditions they went through may be withheld from them this opportunity, but it is education mail modern educational means were produced by the knowledge revolution and the development of electronic technologies and renewable constantly. The study focused on highlighting the most important obstacles that hinder the p

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Zhan, Shanhua, Weijun Sun, and Peipei Kang. "Robust Latent Common Subspace Learning for Transferable Feature Representation." Electronics 11, no. 5 (2022): 810. http://dx.doi.org/10.3390/electronics11050810.

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This paper proposes a novel robust latent common subspace learning (RLCSL) method by integrating low-rank and sparse constraints into a joint learning framework. Specifically, we transform the data from source and target domains into a latent common subspace to perform the data reconstruction, i.e., the transformed source data is used to reconstruct the transformed target data. We impose joint low-rank and sparse constraints on the reconstruction coefficient matrix which can achieve following objectives: (1) the data from different domains can be interlaced by using the low-rank constraint; (2

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49

Xue, Hansheng, Vijini Mallawaarachchi, Yujia Zhang, Vaibhav Rajan, and Yu Lin. "RepBin: Constraint-Based Graph Representation Learning for Metagenomic Binning." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 4 (2022): 4637–45. http://dx.doi.org/10.1609/aaai.v36i4.20388.

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Mixed communities of organisms are found in many environments -- from the human gut to marine ecosystems -- and can have profound impact on human health and the environment. Metagenomics studies the genomic material of such communities through high-throughput sequencing that yields DNA subsequences for subsequent analysis. A fundamental problem in the standard workflow, called binning, is to discover clusters, of genomic subsequences, associated with the constituent organisms. Inherent noise in the subsequences, various biological constraints that need to be imposed on them and the skewed clus

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Wassermann, Gilbert, and Mark Glickman. "Automated Harmonization of Bass Lines from Bach Chorales: A Hybrid Approach." Computer Music Journal 43, no. 2-3 (2020): 142–57. http://dx.doi.org/10.1162/comj_a_00523.

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In this article, a combination of two novel approaches to the harmonization of chorales in the style of J. S. Bach is proposed, implemented, and profiled. The first is the use of the bass line, as opposed to the melody, as the primary input into a chorale-harmonization algorithm. The second is a compromise between methods guided by music knowledge and by machine-learning techniques, designed to mimic the way a music student learns. Specifically, our approach involves learning harmonic structure through a hidden Markov model, and determining individual voice lines by optimizing a Boltzmann pseu

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