Representing, adapting and reasoning with uncertain, imprecise and vague information
This paper presents a formulation for representing and reasoning with uncertain knowledge towards making diagnoses in cases where there is no deep knowledge on the mechanisms that make a set of observations occur, in cases where knowledge about these mechanisms is based on simplifying assumptions or the representation of the underlying mechanisms and their exploitation by a system are resource demanding. Towards this aim, the paper describes a formulation for representing and reasoning with uncertain knowledge. The aim is to provide a generic framework for the development of expert systems that provide assistance to humans during diagnosis. The paper describes in detail the nature of the diagnosis task as part of the exploration task, and identifies the types of uncertainty accommodated in this task. It presents the proposed formulation and provides simple examples to make the mathematical basis more comprehensive. The paper proposes a method for adapting uncertainties to known cases, towards fine-tuning the representation.
uncertain knowledge diagnosis causal diagrams learning