Practical uncertainty assessment of reasoning paths (fault trees) under total uncertainty ignorance

Abstract An engineer needs to know probabilities of different events. He cannot evaluate them using conventional formal calculations (e.g. statistical analysis) because of an information shortage. This paper presents two algorithms which calculate probabilities of a set of terminal hypotheses of a reasoning path (e.g. fault tree). This calculation is based exclusively on common sense metaheuristics and structure (topology) of a reasoning path. Therefore no uncertainty information is required (an assumption of total uncertainty ignorance). The first algorithm introduces a symbolic distance between two knowledge items. If a distance is small then items of uncertainty are similar. The second algorithm is based on a ‘water’ flow through a reasoning path (a long subpath has ‘large resistance’ and therefore low fluid probability). The main advantages of this methodology are its information non-intensity, simplicity and ability to absorb common sense heuristics. Therefore it can easily be put to practical use. A case study (fault tree with 38 terminal hypotheses)is given.