Belaidouni, Somia, Miraoui, Moeiz et Tadj, Chakib.
2022.
« QL-CBR hybrid approach for adapting context-aware services ».
Computer Systems Science and Engineering, vol. 43, nº 3.
pp. 1085-1098.
Compte des citations dans Scopus : 1.
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Résumé
A context-aware service in a smart environment aims to supply services according to user situational information, which changes dynamically. Most exist- ing context-aware systems provide context-aware services based on supervised algorithms. Reinforcement algorithms are another type of machine-learning algo- rithm that have been shown to be useful in dynamic environments through trial- and-error interactions. They also have the ability to build excellent self-adaptive systems. In this study, we aim to incorporate reinforcement algorithms (Q-learn- ing) into a context-aware system to provide relevant services based on a user’s dynamic context. To accelerate the convergence of reinforcement learning (RL) algorithms and provide the correct services in real situations, we propose a com- bination of the Q-learning and case-based reasoning (CBR) algorithms. We then analyze how the incorporation of CBR enables Q-learning to become more effi- cient and adapt to changing environments by continuously producing suitable ser- vices. Simulation results demonstrate the effectiveness of the proposed approach compared to the traditional CBR approach.
| Type de document: | Article publié dans une revue, révisé par les pairs |
|---|---|
| Professeur: | Professeur Tadj, Chakib |
| Affiliation: | Génie électrique |
| Date de dépôt: | 16 juin 2022 20:33 |
| Dernière modification: | 23 juin 2022 15:47 |
| URI: | https://espace2.etsmtl.ca/id/eprint/24515 |
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