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Intelligent systems operate in the midst of a superabun-dance of information lacking the tags that indicate which few aspects are significant to the particular problems at hand at any given time and place. Given this wealth of information coupled with real-time processing constraints, selective attention is fundamental to any chance of success. In much of(More)
The topic of this paper is the exploitation of diversity to enhance computer system reliability. It is well-established that a diverse system composed of multiple alternative versions is more reliable than any single version alone, and this knowledge has occasionally been exploited in safety-critical applications. However, it is not clear what this property(More)
Littlewood and Miller 1989] present a statistical framework for dealing with co-incident failures in multiversion software systems. They develop a theoretical model that holds the promise of high system reliability through the use of multiple, diverse sets of alternative versions. In this paper we adapt their framework to investigate the feasibility of(More)
Multiple Classifier Systems (MCSs) allow evaluation of the uncertainty of classification outcomes that is of crucial importance for safety critical applications. The uncertainty of classification is determined by a trade-off between the amount of data available for training, the classifier diversity and the required performance. The interpretability of MCSs(More)