SUPPORTING COMPLEXITY IN MODELING BAYESIAN TROUBLESHOOTING

Luigi Troiano, Davide De Pasquale

Abstract

Troubleshooting complex systems, such as industrial plants and machinery, is a task entailing an articulated decision making process hard to structure, and generally relying on human experience. Recently probabilistic reasoning, and Bayesian networks in particular, proved to be an effective means to support and drive decisions in Troubleshooting. However, troubleshooting a real system requires to face scalability and feasibility issues, so that the direct employment of Bayesian networks is not feasible. In this paper we report our experience in applying Bayesian approach to industrial case and we propose a methodology to decompose a complex problem in more treatable parts.

References

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Paper Citation


in Harvard Style

Troiano L. and De Pasquale D. (2010). SUPPORTING COMPLEXITY IN MODELING BAYESIAN TROUBLESHOOTING . In Proceedings of the 12th International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-989-8425-05-8, pages 344-349. DOI: 10.5220/0002977703440349


in Bibtex Style

@conference{iceis10,
author={Luigi Troiano and Davide De Pasquale},
title={SUPPORTING COMPLEXITY IN MODELING BAYESIAN TROUBLESHOOTING},
booktitle={Proceedings of the 12th International Conference on Enterprise Information Systems - Volume 2: ICEIS,},
year={2010},
pages={344-349},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002977703440349},
isbn={978-989-8425-05-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - SUPPORTING COMPLEXITY IN MODELING BAYESIAN TROUBLESHOOTING
SN - 978-989-8425-05-8
AU - Troiano L.
AU - De Pasquale D.
PY - 2010
SP - 344
EP - 349
DO - 10.5220/0002977703440349