A methodology for the selection of a paradigm of reasoning under uncertainty in expert system development

The aim of this thesis is to develop a methodology for the selection of a paradigm of reasoning under uncertainty for the expert system developer. This is important since practical information on how to select a paradigm of reasoning under uncertainty is not generally available. The thesis explores the role of uncertainty in an expert system and considers the process of reasoning under uncertainty. The possible sources of uncertainty are investigated and prove to be crucial to some aspects of the methodology. A variety of Uncertainty Management Techniques (UMTs) are considered, including numeric, symbolic and hybrid methods. Considerably more information is found in the literature on numeric methods, than the latter two. Methods that have been proposed for comparing UMTs are studied and comparisons reported in the literature are summarised. Again this concentrates on numeric methods, since there is more literature available. The requirements of a methodology for the selection of a UMT are considered. A manual approach to the selection process is developed. The possibility of extending the boundaries of knowledge stored in the expert system by including meta-data to describe the handling of uncertainty in an expert system is then considered. This is followed by suggestions taken from the literature for automating the process of selection. Finally consideration is given to whether the objectives of the research have been met and recommendations are made for the next stage in researching a methodology for the selection of a paradigm of reasoning under uncertainty in expert system development

The evaluation and enhancement of case driven diagnostic advice systems: a study in three domains

Relevant literature has been reviewed regarding the performance, implementation and evaluation of computer based medical decision support systems. The diagnostic performance of five simple case driven acute chest pain advice systems, have been compared using a standardized set of clinical records. A Bayesian inference model demonstrated superiority over two derived by logistic regression. Small data set flow charts performed well but both relied upon the use of expert opinion. A Bayesian acute abdominal pain diagnostic advice system has been evaluated in a clinical trial. Standardized data collection improved the diagnostic performance of doctors. In practice, the computer system offered little additional user benefit. From further tests in primary care, it was concluded that, whereas general practitioners might enhance their performance by using data collection sheets, paramedics might benefit through direct use of the computer. DERMIS is a new dermatology primary care diagnostic advice system. Components include a database derived from 5203 prospectively collected clinical records, a user interface, and an enhanced Bayesian inference model incorporating combined frequency estimates, expert beliefs and rationalized end-point groups. On laboratory testing, the diagnostic accuracy of DERMIS was 83%. The correct diagnosis appeared in the top three, of a possible 42 disease list on 97% of occasions. In a semi-field trial of DERMIS involving 49 general practitioners, doctors did not always collect the same information as a dermatologist but were able to significantly increase their chance of making a correct diagnosis through use of the computer system. It has been concluded that although implementation of DERMIS might well increase general practitioner diagnostic accuracy and lead to improvements in the management of skin disease in primary care, rates of referral for specialist opinion might not be affected unless standard management plans are adopted

Second generation knowledge based systems in habitat evaluation.

Many expert, or knowledge-based, systems have been constructed in the domain of ecology, several of which are concerned with habitat evaluation. However, these systems have been geared to solving particular problems, with little regard paid to the underlying relationships that exist within a biological system. The implementation of problem-solving methods with little regard to understanding the more primary knowledge of a problem area is referred to in the literature as 'shallow', whilst the representation and utilisation of knowledge of a more fundamental kind is termed 'deep'. This thesis contains the details of a body of research exploring issues that arise from the refinement of traditional expert systems methodologies and theory via the incorporation of depth, along with enhancements in the sophistication of the methods of reasoning (and subsequent effects on the mechanisms of communication between human and computer), and the handling of uncertainty. The approach used to address this research incorporates two distinct aspects. Firstly, the literature of 'depth', expert systems in ecology, uncertainty, and control of reasoning and related user interface issues are critically reviewed, and where inadequacies exist, proposals for improvements are made. Secondly, practical work has taken place involving the construction of two knowledge based systems, one 'traditional', and the other a second generation system. Both systems are primarily geared to the problem of evaluating a pond site with respect to its suitability for the great crested newt (Triturus cristatus). This research indicates that it is possible to build a second-generation knowledge-based system in the domain of ecology, and that construction of the second generation system required a magnitude of effort similar to the firstgeneration system. In addition, it shows that, despite using different architectures and reasoning strategies, such systems may be judged as equally acceptable by endusers, and of similar accuracy in their conclusions. The research also offers guidance concerning the organisation and utilisation of deep knowledge within an expert systems framework, in both ecology and in other domains that have a similar concept-rich nature