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).
References
Brand, D., Zafiropulo, P.: On communicating finite-state machines. J. ACM 30(2), 323–342 (1983). https://doi.org/10.1145/322374.322380
Cassandras, C., Lafortune, S.: Introduction to Discrete Event Systems, 2nd edn. Springer, New York (2008)
Hopcroft, J., Motwani, R., Ullman, J.: Introduction to Automata Theory, Languages, and Computation, 3rd edn. Addison-Wesley, Reading (2006)
Jéron, T., Marchand, H., Pinchinat, S., Cordier, M.: Supervision patterns in discrete event systems diagnosis. In: Workshop on Discrete Event Systems (WODES 2006), pp. 262–268. IEEE Computer Society, Ann Arbor (2006)
Lamperti, G., Zanella, M.: Context-sensitive diagnosis of discrete-event systems. In: Walsh, T. (ed.) Twenty-Second International Joint Conference on Artificial Intelligence (IJCAI 2011), vol. 2, pp. 969–975. AAAI Press, Barcelona (2011)
Lamperti, G., Zanella, M., Zhao, X.: Introduction to Diagnosis of Active Systems. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-92733-6
Lamperti, G., Zhao, X.: Diagnosis of active systems by semantic patterns. IEEE Trans. Syst. Man Cybern.: Syst. 44(8), 1028–1043 (2014). https://doi.org/10.1109/TSMC.2013.2296277
Sampath, M., Sengupta, R., Lafortune, S., Sinnamohideen, K., Teneketzis, D.: Diagnosability of discrete-event systems. IEEE Trans. Autom. Control 40(9), 1555–1575 (1995)
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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