Combining case based reasoning with neural networks

This paper presents a neural network based technique for mapping problem situations to problem solutions for Case-Based Reasoning (CBR) applications. Both neural networks and CBR are instance-based learning techniques, although neural nets work with numerical data and CBR systems work with symbolic data. This paper discusses how the application scope of both paradigms could be enhanced by the use of hybrid concepts. To make the use of neural networks possible, the problem's situation and solution features are transformed into continuous features, using techniques similar to CBR's definition of similarity metrics. Radial Basis Function (RBF) neural nets are used to create a multivariable, continuous input-output mapping. As the mapping is continuous, this technique also provides generalisation between cases, replacing the domain specific solution adaptation techniques required by conventional CBR. This continuous representation also allows, as in fuzzy logic, an associated membership measure to be output with each symbolic feature, aiding the prioritisation of various possible solutions. A further advantage is that, as the RBF neurons are only active in a limited area of the input space, the solution can be accompanied by local estimates of accuracy, based on the sufficiency of the cases present in that area as well as the results measured during testing. We describe how the application of this technique could be of benefit to the real world problem of sales advisory systems, among others

Who do they think they are? The changing identities of professional administrators and managers in UK higher education

Contemporary universities, serving mass higher education markets, find themselves delivering complex, broadly based projects such as student support and welfare, human resource development, and business enterprise. Established concepts of academic administration and devolved management have been overlaid by more fluid institutional structures and cultures, with a softening of internal and external boundaries (Whitchurch 2004; 2005). These developments have caused major shifts in the identities of professional administrators and managers, as they adopt more projectoriented roles crossing functional and organisational boundaries. This paper considers the dynamics of these changes, in terms that move beyond conventional assumptions about ‘administration’ and ‘management’. While identities have been defined traditionally via structured domains such as professional knowledges, institutional boundaries, and the policy requirements of the higher education sector, an emergent ‘project’ domain has fostered the development of an increasingly multi-professional grouping of staff, with implications for career futures

What is Computational Intelligence and where is it going?

What is Computational Intelligence (CI) and what are its relations with Artificial Intelligence (AI)? A brief survey of the scope of CI journals and books with ``computational intelligence'' in their title shows that at present it is an umbrella for three core technologies (neural, fuzzy and evolutionary), their applications, and selected fashionable pattern recognition methods. At present CI has no comprehensive foundations and is more a bag of tricks than a solid branch of science. The change of focus from methods to challenging problems is advocated, with CI defined as a part of computer and engineering sciences devoted to solution of non-algoritmizable problems. In this view AI is a part of CI focused on problems related to higher cognitive functions, while the rest of the CI community works on problems related to perception and control, or lower cognitive functions. Grand challenges on both sides of this spectrum are addressed