2018
D. C. Tsouros; K. Stergiou; P. G. Sarigiannidis
Efficient methods for constraint acquisition Book Chapter
In: Lecture Notes in Computer Science, vol. 11008 LNCS, pp. 373–388, Springer International Publishing, 2018.
Περίληψη | BibTeX | Ετικέτες: Constraint acquisition, Learning, Modeling | Σύνδεσμοι:
@inbook{Tsouros2018373,
title = {Efficient methods for constraint acquisition},
author = { D. C. Tsouros and K. Stergiou and P. G. Sarigiannidis},
url = {https://www.researchgate.net/publication/325967779_Efficient_Methods_for_Constraint_Acquisition},
doi = {10.1007/978-3-319-98334-9_25},
year = {2018},
date = {2018-01-01},
booktitle = {Lecture Notes in Computer Science},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {11008 LNCS},
pages = {373--388},
publisher = {Springer International Publishing},
abstract = {Constraint acquisition systems such as QuAcq and MultiAcq can assist non-expert users to model their problems as constraint networks by classifying (partial) examples as positive or negative. For each negative example, the former focuses on one constraint of the target network, while the latter can learn a maximum number of constraints. Two bottlenecks of the acquisition process where both these algorithms encounter problems are the large number of queries required to reach convergence, and the high cpu times needed to generate queries, especially near convergence. We propose methods that deal with both these issues. The first one is an algorithm that blends the main idea of MultiAcq into QuAcq resulting in a method that learns as many constraints as MultiAcq does after a negative example, but with a lower complexity. The second is a technique that helps reduce the number of queries significantly. The third is based on the use of partial queries to cut down the time required for convergence. Experiments demonstrate that our resulting algorithm, which integrates all the new techniques, does not only generate considerably fewer queries than QuAcq and MultiAcq, but it is also by far faster than both of them, both in average query generation time and in total run time. © Springer Nature Switzerland AG 2018.},
keywords = {Constraint acquisition, Learning, Modeling},
pubstate = {published},
tppubtype = {inbook}
}
2014
M. Louta; P. Sarigiannidis; S. Misra; P. Nicopolitidis; G. Papadimitriou
RLAM: A dynamic and efficient reinforcement learning-based adaptive mapping scheme in mobile WiMAX networks Journal Article
In: Mobile Information Systems, vol. 10, no. 2, pp. 173-196, 2014.
Περίληψη | BibTeX | Ετικέτες: channel allocation ratio, IEEE 802.16, Learning, mapping, OFDMA, WiMAX | Σύνδεσμοι:
@article{Louta2014173,
title = {RLAM: A dynamic and efficient reinforcement learning-based adaptive mapping scheme in mobile WiMAX networks},
author = { M. Louta and P. Sarigiannidis and S. Misra and P. Nicopolitidis and G. Papadimitriou},
url = {https://www.researchgate.net/publication/259965632_RLAM_A_Dynamic_and_Efficient_Reinforcement_Learning-based_Adaptive_Mapping_Scheme_in_Mobile_WiMAX_Networks},
doi = {10.3233/MIS-130177},
year = {2014},
date = {2014-01-01},
journal = {Mobile Information Systems},
volume = {10},
number = {2},
pages = {173-196},
abstract = {WiMAX (Worldwide Interoperability for Microwave Access) constitutes a candidate networking technology towards the 4G vision realization. By adopting the Orthogonal Frequency Division Multiple Access (OFDMA) technique, the latest IEEE 802.16x amendments manage to provide QoS-aware access services with full mobility support. A number of interesting scheduling and mapping schemes have been proposed in research literature. However, they neglect a considerable asset of the OFDMA-based wireless systems: the dynamic adjustment of the downlink-to-uplink width ratio. In order to fully exploit the supported mobile WiMAX features, we design, develop, and evaluate a rigorous adaptive model, which inherits its main aspects from the reinforcement learning field. The model proposed endeavours to efficiently determine the downlink-to-uplink width ratio, on a frame-by-frame basis, taking into account both the downlink and uplink traffic in the Base Station (BS). Extensive evaluation results indicate that the model proposed succeeds in providing quite accurate estimations, keeping the average error rate below 15% with respect to the optimal sub-frame configurations. Additionally, it presents improved performance compared to other learning methods (e.g., learning automata) and notable improvements compared to static schemes that maintain a fixed predefined ratio in terms of service ratio and resource utilization. © 2014-IOS Press.},
keywords = {channel allocation ratio, IEEE 802.16, Learning, mapping, OFDMA, WiMAX},
pubstate = {published},
tppubtype = {article}
}
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