Generally, human users provide evidence assessments for intelligent systems. Users see or hear data D and determine if it is evidence of the type E or not E. Unfortunately, since expert systems often model complex and ambiguous processes, there is a probability distribution that D will be categorized as E or not E (E′). This evidence categorization problem is referred to in this paper as semantic ambiguity. The purpose of this paper is to model the impact of semantic ambiguity in the context of a well-known set of weights. In particular, this paper uses the Bayesian AL/X weights as the basis of that model. The resulting model shows that semantic ambiguity can have a substantial impact on the resulting probabilities. The same approach can be extended to other forms of weights on rules or other similar structures.
