TY  - EJOU
AU  - Belaidouni, Somia 
AU  - Miraoui, Moeiz 
AU  - Tadj, Chakib 

TI  - QL-CBR Hybrid Approach for Adapting Context-Aware Services
T2  - Computer Systems Science and Engineering

PY  - 2022
VL  - 43
IS  - 3
SN  - 

AB  - 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.
KW  - Context-aware service; smart space; auto-adaptation; reinforcement learning; Q-learning; supervised learning; CBR

DO  - 10.32604/csse.2022.024056