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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)
1. The challenge A gateway event [35] is a change to a system that leads to the possibility of huge increases in kinds and levels of complexity. It opens up a whole new kind of phase space to the system's dynamics. Gateway events during evolution of life on earth include the appearance of eukaryotes (organisms with a cell nucleus), an oxygen atmosphere,(More)
1. The challenge Our Grand Challenge for computer science is: to journey through the gateway event obtained by breaking our current classical computational assumptions, and thereby develop a mature science of Non-Classical Computation. The background and rationale of the challenge is discussed in the first part of this paper: I: A Grand Challenge for(More)
The uncertainty of classification outcomes is of crucial importance for many safety critical applications including, for example, medical diagnostics. In such applications the uncertainty of classification can be reliably estimated within a Bayesian model averaging technique that allows the use of prior information. Decision Tree (DT) classification models(More)