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A decentralised symbolic diagnosis approach. (English) Zbl 1211.93086
Coelho, Helder (ed.) et al., ECAI 2010. 19th European conference on artificial intelligence, August 16–20, 2010 Lisbon, Portugal. Including proceedings of the 6th prestigious applications of artificial intelligence (PAIS-2010). Amsterdam: IOS Press (ISBN 978-1-60750-605-8/pbk; 978-1-60750-606-5/ebook). Frontiers in Artificial Intelligence and Applications 215, 99-104 (2010).
Summary: This paper considers the diagnosis of large discrete-event systems consisting of many components. The problem is to determine, online, all failures and states that explain a given sequence of observations. Several model-based diagnosis approaches deal with this problem but they usually have either poor time performance or result in space explosion. Recent work has shown that both problems can be tackled when encoding diagnosis approaches symbolically by means of binary decision diagrams. This paper further improves upon these results and presents a decentralised symbolic diagnosis method that computes the diagnosis information for each component off-line and then combines them on-line. Experimental results show that our method provides significant improvements over existing approaches.
For the entire collection see [Zbl 1207.68003].
MSC:
93C65 Discrete event control/observation systems
68W30 Symbolic computation and algebraic computation
90B25 Reliability, availability, maintenance, inspection in operations research
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