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

Biomedical applications of belief networks

Biomedicine is an area in which computers have long been expected to play a significant role. Although many of the early claims have proved unrealistic, computers are gradually becoming accepted in the biomedical, clinical and research environment. Within these application areas, expert systems appear to have met with the most resistance, especially when applied to image interpretation.In order to improve the acceptance of computerised decision support systems it is necessary to provide the information needed to make rational judgements concerning the inferences the system has made. This entails an explanation of what inferences were made, how the inferences were made and how the results of the inference are to be interpreted. Furthermore there must be a consistent approach to the combining of information from low level computational processes through to high level expert analyses.nformation from low level computational processes through to high level expert analyses. Until recently ad hoc formalisms were seen as the only tractable approach to reasoning under uncertainty. A review of some of these formalisms suggests that they are less than ideal for the purposes of decision making. Belief networks provide a tractable way of utilising probability theory as an inference formalism by combining the theoretical consistency of probability for inference and decision making, with the ability to use the knowledge of domain experts.nowledge of domain experts. The potential of belief networks in biomedical applications has already been recog¬ nised and there has been substantial research into the use of belief networks for medical diagnosis and methods for handling large, interconnected networks. In this thesis the use of belief networks is extended to include detailed image model matching to show how, in principle, feature measurement can be undertaken in a fully probabilistic way. The belief networks employed are usually cyclic and have strong influences between adjacent nodes, so new techniques for probabilistic updating based on a model of the matching process have been developed.An object-orientated inference shell called FLAPNet has been implemented and used to apply the belief network formalism to two application domains. The first application is model-based matching in fetal ultrasound images. The imaging modality and biological variation in the subject make model matching a highly uncertain process. A dynamic, deformable model, similar to active contour models, is used. A belief network combines constraints derived from local evidence in the image, with global constraints derived from trained models, to control the iterative refinement of an initial model cue.In the second application a belief network is used for the incremental aggregation of evidence occurring during the classification of objects on a cervical smear slide as part of an automated pre-screening system. A belief network provides both an explicit domain model and a mechanism for the incremental aggregation of evidence, two attributes important in pre-screening systems.Overall it is argued that belief networks combine the necessary quantitative features required of a decision support system with desirable qualitative features that will lead to improved acceptability of expert systems in the biomedical domain

Uncertainty management for coastal defence systems.

SIGLEAvailable from British Library Document Supply Centre-DSC:DXN029923 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Tratamento de imprecisão em sistemas especialistas

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia de Produção, Florianópolis, 1991.Esta dissertação apresenta um levantamento do estado da arte no Tratamento de Imprecisão em Sistemas Especialistas. Aborda-se o Raciocínio Humano na Resolução de Problemas e as principais técnicas existentes em tratamento de imprecisão em Inteligência Artificial: Método Bayesiano, Fatores de Certeza, Teoria da Evidência de Dempster e Shafer e Teoria dos Conjuntos Difusos. Para cada uma das técnicas estudadas são apresentados seus fundamentos teóricos, exemplos práticos e uma discussão sobre a performance entre as técnicas em relação aos principais requerimentos a uma técnica ideal no tratamento de imprecisão em Sistemas Especialistas

A new approach for modelling uncertainty in expert systems knowledge bases

The current paradigm of modelling uncertainty in expert systems knowledge bases using Certainty Factors (CF) has been critically evaluated. A way to circumvent the awkwardness, non-intuitiveness and constraints encountered while using CF has been proposed. It is based on introducing Data Marks for askable conditions and Data Marks for conclusions of relational models, followed by choosing the best suited way to propagate those Data Marks into Data Marks of rule conclusions. This is done in a way orthogonal to the inference using Aristotelian Logic. Using Data Marks instead of Certainty Factors removes thus the intellectual discomfort caused by rejecting the notion of truth, falsehood and the Aristotelian law of excluded middle, as is done when using the CF methodology. There is also no need for changing the inference system software (expert system shell): the Data Marks approach can be implemented by simply modifying the knowledge base that should accommodate them. The methodology of using Data Marks to model uncertainty in knowledge bases has been illustrated by an example of SWOT analysis of a small electronic company. A short summary of SWOT analysis has been presented. The basic data used for SWOT analysis of the company are discussed. The rmes_EE SWOT knowledge base consisting of a rule base and model base have been presented and explained. The results of forward chaining for this knowledge base have been presented and critically evaluated