Abstract
Case-based reasoning is an intuitive approach to problem-solving in artificial intelligence that involves reusing existing experience, including solutions to problems or mechanisms to derive them. However, current Case-based reasoning systems suffer from a lack of generality due to their heavy reliance on domain-specific knowledge, and they often struggle with the adaptation process, which is driven by the application domain. In addition, these systems often perform poorly as they treat each step of the Case-based reasoning methodology separately and independently. To address these limitations, this work proposes a domain-independent Case-based reasoning framework that integrates each step of the reasoning process to support other stages of the process. The framework is evaluated in an experimental setting on a sober consumption energy system in buildings, demonstrating its effectiveness.
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Boulmaiz, F., Reignier, P., Ploix, S. (2023). Reducing Reliance on Domain Knowledge in Case-Based Reasoning. In: Fujita, H., Wang, Y., Xiao, Y., Moonis, A. (eds) Advances and Trends in Artificial Intelligence. Theory and Applications. IEA/AIE 2023. Lecture Notes in Computer Science(), vol 13925. Springer, Cham. https://doi.org/10.1007/978-3-031-36819-6_1
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DOI: https://doi.org/10.1007/978-3-031-36819-6_1
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