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Relevant bibliographies by topics / Learning from Constraints

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Journal articles

Academic literature on the topic 'Learning from Constraints'

Author: Grafiati

Published: 10 March 2023

Last updated: 14 March 2023

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Journal articles on the topic "Learning from Constraints"

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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