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- W201900349 abstract "Abstract This paper presents the introduction of Artificial Intelligence (AI) techniques into the field of Computer-Aided Testing (CAT) and Computer-Aided Repairing (CAR). Such techniques are useful since it is increasingly important to improve the diagnostic capabilities of Automatic Test Equipments (ATE) and AI seems a suitable response. The overall goal is to set up Test/Repair environments which guarantee not only high diagnostic capability, but also high productivity. The system presented in this paper is based on a loop Tester→Repair Station→Tester . In a conventional approach, data coming from a Repair Station are not used to improve the Tester's performances. In our solution, data are first validated by the Tester and successively processed in order to gather new knowledge about real cases. Such knowledge is learned by the Tester to improve its symptom interpretative capabilities. Two methodological aspects are relevant in this approach: the knowledge the Tester receives from the Repair Station and the knowledge the Tester itself needs to learn from experience. It is consequently necessary to organize knowledge in suitable knowledge bases and to use it with a proper reasoning mechanism. AI techniques are used to achieve both goals: from one hand they allow us to formalize the problem in a rigorous way, from the other hand they facilitate the generation of an open system, in which the introduction of new diagnostic rules is easy and where a real feedback from the industrial testing environment is possible. Thus, AI contributes to the improvement of diagnostic capabilities in a twofold way: it helps the ATEs in overcoming symptom ambiguity and in learning from experience how to modify the fault finding procedures." @default.
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- W201900349 date "1988-06-01" @default.
- W201900349 modified "2023-09-26" @default.
- W201900349 title "Application of AI Techniques in Car Environments" @default.
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- W201900349 doi "https://doi.org/10.1016/s1474-6670(17)54501-2" @default.
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