  Designing smart home services is a complex task when multiple services with a
large number of sensors and actuators are deployed simultaneously. It may rely
on knowledge-based or data-driven approaches. The former can use rule-based
methods to design services statically, and the latter can use learning methods
to discover inhabitants' preferences dynamically. However, neither of these
approaches is entirely satisfactory because rules cannot cover all possible
situations that may change, and learning methods may make decisions that are
sometimes incomprehensible to the inhabitant. In this paper, PBRE (Pedagogic
Based Rule Extractor) is proposed to extract rules from learning methods to
realize dynamic rule generation for smart home systems. The expected advantage
is that both the explainability of rule-based methods and the dynamicity of
learning methods are adopted. We compare PBRE with an existing rule extraction
method, and the results show better performance of PBRE. We also apply PBRE to
extract rules from a smart home service represented by an NRL (Neural
Network-based Reinforcement Learning). The results show that PBRE can help the
NRL-simulated service to make understandable suggestions to the inhabitant.
