Abstract
Computing all minimal hitting sets is one of the key steps in model-based diagnosis. Because of the low capabilities due to the expansion of state space in large-scale system diagnosis, more efficient approximation algorithms are in motivation. A matrix-based minimal hitting set (M-MHS) algorithm is proposed in this paper. A parameter matrix records the relationships between elements and sets and the initial problem is divided into several sub-problems by decomposition. The efficient prune rules avoid the computation of the sub-problems without solutions. Parameterized way and de-parameterized way are both given so that the more suitable algorithm could be chosen according to the cases. The simulation results show that, the proposed algorithm outperforms HSSE and BNB-HSSE in large-scale problems and keeps a relatively stable performance when data changes in different regulations. The algorithm provides a valuable tool for computing hitting sets in model-based diagnosis of large-scale systems.
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© 2012 Springer-Verlag Berlin Heidelberg
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Wang, D., Feng, W., Li, J., Zhang, M. (2012). A Parameter Matrix Based Approach to Computing Minimal Hitting Sets. In: Ding, W., Jiang, H., Ali, M., Li, M. (eds) Modern Advances in Intelligent Systems and Tools. Studies in Computational Intelligence, vol 431. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30732-4_10
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DOI: https://doi.org/10.1007/978-3-642-30732-4_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-30731-7
Online ISBN: 978-3-642-30732-4
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