Open Access

ARTICLE

QL-CBR Hybrid Approach for Adapting Context-Aware Services

Somia Belaidouni^1,2, Moeiz Miraoui^3,4,*, Chakib Tadj¹

1 Ecole de Technologie Supérieure, Montréal, QCH3C1K3, Canada
2 Faculty of Exact and Applied Sciences, University of Oran 1, Oran, 31000, Algeria
3 Higher Institute of Sciences and Technologies, University of Gafsa, Gafsa, 2100, Tunisia
4 AL-Lith Computer College, Umm Al-Qura University, Al-Lith, 28434, Saudi Arabia

* Corresponding Author: Moeiz Miraoui. Email:

Computer Systems Science and Engineering 2022, 43(3), 1085-1098. https://doi.org/10.32604/csse.2022.024056

Received 01 October 2021; Accepted 11 November 2021; Issue published 09 May 2022

Abstract

A context-aware service in a smart environment aims to supply services according to user situational information, which changes dynamically. Most existing context-aware systems provide context-aware services based on supervised algorithms. Reinforcement algorithms are another type of machine-learning algorithm 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-learning) 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 combination of the Q-learning and case-based reasoning (CBR) algorithms. We then analyze how the incorporation of CBR enables Q-learning to become more efficient and adapt to changing environments by continuously producing suitable services. Simulation results demonstrate the effectiveness of the proposed approach compared to the traditional CBR approach.

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Keywords

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