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The success of smart environments largely depends on their smartness ofunderstanding the environments' ongoing situations. Accordingly, this task isan essence to smart environment central processors. Obtaining knowledge fromthe environment is often through sensors, and the response to a particularcircumstance is offered by actuators. This can be improved by getting userfeedback, and capturing environmental changes. Machine learning techniques andsemantic reasoning tools are widely used in this area to accomplish the goal ofinterpretation. In this paper, we have proposed a hybrid approach utilizingboth machine learning and semantic reasoning tools to derive a betterunderstanding from sensors. This method uses situation templates jointly with adecision tree to adapt the system knowledge to the environment. To test thisapproach we have used a simulation process which has resulted in a betterprecision for detecting situations in an ongoing environment involving livingagents while capturing its dynamic nature.

Hybrid-Learning approach toward situation recognition and handling