Decision-making Improvement in Dynamic Environments Using Machine Learning

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