Abstract
Offline knowledge compilation enables an online diagnosis process that can manage in a linear time any sequence of observables. In a posteriori diagnosis, this sequence, called a symptom, is the input, and the corresponding collection of sets of faults, each set being a candidate, is the output. Since the compilation is computationally hard, we propose to compile only the knowledge chunks that are relevant to some phenomena of interest, each described as a scenario. If, on the one hand, a partial knowledge compilation does not ensure the completeness of the resulting collection of candidates, on the other, it allows attention to be focused on the most important of them. Moreover, the compiled structure, called symptom dictionary, can incrementally be extended over time.
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Notes
- 1.
Each DFA state here includes only the significant NFA states. A state is significant when it is either final or it is exited by a transition marked with a (non null) observation.
- 2.
Each state of the DFA includes only the significant states of the NFA (cf. Footnote 1).
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Acknowledgments
This work was supported in part by Lombardy Region (Italy), project Smart4CPPS, Linea Accordi per Ricerca, Sviluppo e Innovazione, POR-FESR 2014-2020 Asse I.
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Bertoglio, N., Lamperti, G., Zanella, M. (2019). A Posteriori Diagnosis of Discrete-Event Systems with Symptom Dictionary and Scenarios. In: Wotawa, F., Friedrich, G., Pill, I., Koitz-Hristov, R., Ali, M. (eds) Advances and Trends in Artificial Intelligence. From Theory to Practice. IEA/AIE 2019. Lecture Notes in Computer Science(), vol 11606. Springer, Cham. https://doi.org/10.1007/978-3-030-22999-3_29
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DOI: https://doi.org/10.1007/978-3-030-22999-3_29
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