Automatic Rule Generation for Decision-Making in Context-Aware Systems Using Machine Learning.

With the increasing interest devoted to dynamic environments, a crucial aspect is revealed in context-aware systems to deal with the rapid changes occurring in users' surrounding environments at runtime. However, most context-aware systems with predefined context-aware rules may not support effective decision-making in dynamic environments. These context-aware rules, which take into account different context information to reach an appropriate decision, could lose their efficiency at runtime. Therefore, a growing need is emerging to address the decision-making issue leveraged by dynamic environments. To tackle this issue, we present an approach that relies on improving decision-making in the wake of dynamic environments through automatically enriching a rule knowledge base with new context-aware rules discovered at runtime. The major features of the presented approach are as follows: (i) a hybridization of two machine learning algorithms for rule generation, (ii) an extended genetic algorithm (GA) for rule optimization, and (iii) a rule transformation for the knowledge base enrichment in an automated manner. Furthermore, extensive experiments on different datasets are performed to assess the effectiveness of the presented approach. The obtained experimental results depict that this approach exhibits better effectiveness compared to some algorithms and state-of-the-art works.