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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\u2019 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.<\/jats:p>","DOI":"10.1007\/978-3-031-12426-6_13","type":"book-chapter","created":{"date-parts":[[2022,7,28]],"date-time":"2022-07-28T16:10:03Z","timestamp":1659024603000},"page":"158-173","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["PBRE: A Rule Extraction Method from\u00a0Trained Neural Networks Designed for\u00a0Smart Home Services"],"prefix":"10.1007","author":[{"given":"Mingming","family":"Qiu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Elie","family":"Najm","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"R\u00e9mi","family":"Sharrock","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bruno","family":"Traverson","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,7,29]]},"reference":[{"issue":"3","key":"13_CR1","doi-asserted-by":"publisher","first-page":"1639","DOI":"10.1016\/S0362-546X(96)00267-2","volume":"30","author":"AD Arbatli","year":"1997","unstructured":"Arbatli, A.D., Akin, H.L.: Rule extraction from trained neural networks using genetic algorithms. 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