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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.

Knowledge Acquisition has long been acknowledged as the bottleneck in producing Expert Systems. This is because, until relatively recently, the KA (Knowledge Acquisition) process has concentrated on extracting knowledge from a domain expert, which is a very time consuming process. Support tools have been constructed to help this process, but these have not been able to reduce the time radically. However, in many domains, the expert is not the only source of knowledge, nor indeed the best source of knowledge. This is particularly true in industrial settings where performance information is routinely archived. This information, if processed correctly, can provide a substantial part of the knowledge required to build a KB (Knowledge Base). In this thesis I discuss current KA approaches and then go on to outline a methodology which uses KD (Knowledge Discovery) techniques to mine archived time series data to produce fault detection and diagnosis KBs with <I>minimal expert input. </I>This methodology is implemented in the TIGON system, which is the focus of this thesis. TIGON uses archived information (in TIGON's case the information is from a gas turbine engine) along with <I>guidance</I> from the expert to produce KBs for detecting and diagnosing faults in a gas turbine engine. TIGON's performance is also analysed in some detail. A comparison with other related work is also included.

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