QL-CBR hybrid approach for adapting context-aware services

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.

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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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