Decision-making Improvement in Dynamic Environments Using Machine Learning

The proliferation of ubiquitous computing with smartphones makes context models and information extremely rich dynamic on account highly environments. However, defining rules at design time may impair their efficiency the decision-making process runtime. Therefore, it is important to address problems leveraged by environments context-model evolution. In this sense, a solution that could emerge continuous rule knowledge base evolution paper, we propose decision adaptation component relies generating new due changes occurring around users This aims support in alleviate human effort needed infer rules. A case study was conducted illustrate implementation proposed for database enrichment improvement. Moreover, an experimental evaluation provided assess effectiveness component. results show exhibits better than other well-known algorithms state-of-the-art approaches. Doi: 10.28991/HEF-2022-03-01-04 Full Text: PDF