TOWARDS A THEORY OF FUZZY CAUSALITY FOR DIAGNOSIS

Parsimonious covering offers an alternative to rules for building diag nostic expert systems. Abductive paradigms, such as parsimonious cover ing, are a departure from the forward-chaining, rule-based approach, w hich is based on deduction. Parsimonious covering addresses weaknesses of rule-based systems where the diagnosis may contain multiple faults or disorders, or where the need to include all the necessary context for each rule's application in the antecedent clauses of each rule wou ld make the representation of the knowledge base too large or overly c omplex. In this paper, we compare the notions of deterministic coverin g and the probabilistic causal model with two fuzzy analogies: fuzzy s ubsethood and fuzzy similarity. Monotonic upper and lower bounds for f uzzy similarity are derived, and pruning opportunities are identified for search through the power set of disorders, given a measured, crisp manifestation set.