Handling uncertainty in self-adaptive systems: an ontology-based reinforcement learning model-Reference-Cited by-同舟云学术

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Handling uncertainty in self-adaptive systems: an ontology-based reinforcement learning model

Published:2023-01-06 Issue: Volume: Page:
ISSN:2199-4668
Container-title:Journal of Reliable Intelligent Environments
language:en
Short-container-title:J Reliable Intell Environ

Author:

Ghanadbashi Saeedeh^ORCID,Safavifar Zahra,Taebi Farshad,Golpayegani Fatemeh

Publisher

Springer Science and Business Media LLC

Subject

Renewable Energy, Sustainability and the Environment,Artificial Intelligence,Computer Science Applications,Computer Networks and Communications

Link

https://link.springer.com/content/pdf/10.1007/s40860-022-00198-x.pdf

Reference66 articles.

1. Calinescu R, Mirandola R, Perez-Palacin D, Weyns D (2020) Understanding uncertainty in self-adaptive systems. In: International conference on autonomic computing and self-organizing systems (ACSOS). IEEE, Washington, pp 242–251

2. Van Der Donckt J, Weyns D, Quin F, Van Der Donckt J, Michiels S (2020) Applying deep learning to reduce large adaptation spaces of self-adaptive systems with multiple types of goals. In: International symposium on software engineering for adaptive and self-managing systems (SEAMS). IEEE/ACM, Seoul, pp 20–30

3. Wang Y, Yang X, Liang H, Liu Y (2018) A review of the self-adaptive traffic signal control system based on future traffic environment. J Adv Transp 2018

4. Safavifar Z, Ghanadbashi S, Golpayegani F (2021) Adaptive workload orchestration in pure edge computing: a reinforcement-learning model. In: International conference on tools with artificial intelligence (ICTAI). IEEE, Washington, pp 856–860

5. Ciampi M, Coronato A, Naeem M, Silvestri S (2022) An intelligent environment for preventing medication errors in home treatment. Expert Syst Appl 193:116434

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