@Article{csse.2022.024056,
AUTHOR = {Somia Belaidouni, Moeiz Miraoui, Chakib Tadj},
TITLE = {QL-CBR Hybrid Approach for Adapting Context-Aware Services},
JOURNAL = {Computer Systems Science and Engineering},
VOLUME = {43},
YEAR = {2022},
NUMBER = {3},
PAGES = {1085--1098},
URL = {http://www.techscience.com/csse/v43n3/47687},
ISSN = {},
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.},
DOI = {10.32604/csse.2022.024056}
}