HKD-SHO: A hybrid smart home system based on knowledge-based and
  data-driven services
Mingming Qiu Elie Najm Rémi Sharrock Bruno Traverson
A smart home is realized by setting up various services. Several methods have
been proposed to create smart home services, which can be divided into
knowledge-based and data-driven approaches. However, knowledge-based approaches
usually require manual input from the inhabitant, which can be complicated if
the physical phenomena of the concerned environment states are complex, and the
inhabitant does not know how to adjust related actuators to achieve the target
values of the states monitored by services. Moreover, machine learning-based
data-driven approaches that we are interested in are like black boxes and
cannot show the inhabitant in which situations certain services proposed
certain actuators' states. To solve these problems, we propose a hybrid system
called HKD-SHO (Hybrid Knowledge-based and Data-driven services based Smart
HOme system), where knowledge-based and machine learning-based data-driven
services are profitably integrated. The principal advantage is that it inherits
the explicability of knowledge-based services and the dynamism of data-driven
services. We compare HKD-SHO with several systems for creating dynamic smart
home services, and the results show the better performance of HKD-SHO.