Learn More
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)
In this paper we critically survey the AI programs that have been developed to exhibit some aspect of creative behaviour. We describe five necessary characteristics of models of creativity, and we apply these characteristics to help assess the programs surveyed. These characteristic features also provide a basis for a new theory of creative behavior: an(More)
Littlewood and Miller [4] present a statistical framework for dealing with coincident 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)
In earlier studies of multiversion programming, both empirical and analytical, emphasis switched from notions of independence to one of minimization of coincident failure. We show that neither independence of failure, nor lack of coincident failure are the single important properties. Indeed, an N-version system may deliver an optimal performance (under(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)
For a variety of reasons, the relative impacts of neural-net inputs on the output of a network’s computation is valuable information to obtain. In particular, it is desirable to identify the significant features, or inputs, of a data-defined problem before the data is sufficiently preprocessed to enable high performance neural-net training. We have defined(More)