PBRE: A Rule Extraction Method from Trained Neural Networks Designed for Smart Home Services

TL;DR

This paper introduces PBRE, a rule extraction method from trained neural networks, enabling dynamic, explainable rule generation for smart home services by combining the strengths of rule-based and learning-based approaches.

Contribution

PBRE is a novel rule extraction technique that improves explainability and adaptability in smart home systems by deriving rules from neural networks.

Findings

PBRE outperforms existing rule extraction methods in accuracy.

PBRE enables neural network-based smart home services to provide understandable suggestions.

Application of PBRE to NRL demonstrates effective rule generation for dynamic smart home environments.

Abstract

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.