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Book Sections Year : 2022

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

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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.
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Dates and versions

hal-03860813 , version 1 (18-11-2022)

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Mingming Qiu, Elie Najm, Rémi Sharrock, Bruno Traverson. PBRE: A Rule Extraction Method from Trained Neural Networks Designed for Smart Home Services. Christine Strauss; Alfredo Cuzzocrea; Gabriele Kotsis; A Min Tjoa; Ismail Khalil. Database and Expert Systems Applications. 33rd International Conference, DEXA 2022, Vienna, Austria, August 22–24, 2022, Proceedings, Part II, 13427, Springer International Publishing, pp.158-173, 2022, Lecture Notes in Computer Science, 978-3-031-12425-9. ⟨10.1007/978-3-031-12426-6_13⟩. ⟨hal-03860813⟩
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