  The success of smart environments largely depends on their smartness of
understanding the environments' ongoing situations. Accordingly, this task is
an essence to smart environment central processors. Obtaining knowledge from
the environment is often through sensors, and the response to a particular
circumstance is offered by actuators. This can be improved by getting user
feedback, and capturing environmental changes. Machine learning techniques and
semantic reasoning tools are widely used in this area to accomplish the goal of
interpretation. In this paper, we have proposed a hybrid approach utilizing
both machine learning and semantic reasoning tools to derive a better
understanding from sensors. This method uses situation templates jointly with a
decision tree to adapt the system knowledge to the environment. To test this
approach we have used a simulation process which has resulted in a better
precision for detecting situations in an ongoing environment involving living
agents while capturing its dynamic nature.
