PBRE: A Rule Extraction Method from Trained Neural Networks Designed for Smart Home Services
This work addresses the need for explainable and dynamic decision-making in smart home systems, though it appears incremental as it builds on existing rule extraction techniques.
The authors tackled the challenge of designing smart home services by proposing PBRE, a method to extract rules from trained neural networks, which achieved better performance compared to an existing rule extraction method and enabled understandable suggestions in a neural network-based reinforcement learning simulation.
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.