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Expert systems offer an effective alternative for representing specialized knowledge and supporting decision-making through production rules (Russell & Norvig, 2021). This paper presents NetExpert-XAI, an expert system for the explainable diagnosis of faults in local area networks (LANs). The methodology was based on knowledge engineering principles, encompassing knowledge acquisition and representation, the design of a knowledge base, and the implementation of a forward-chaining inference engine using Python and the Experta library (Experta Developers, n.d.). The system integrates a user interface, a fact base, an inference engine, and an explanation module, enabling the generation of justified diagnoses and technical recommendations in accordance with the principles of Explainable Artificial Intelligence (Adadi & Berrada, 2018). The results demonstrate that the prototype effectively replicates the reasoning of network administration specialists and serves as a transparent, scalable tool to support the diagnostic process.
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Lopez et al. (2026) studied this question.
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