From uncertainty to approximate reasoning: part 2: reasoning with algorithmic rules

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Abstract
This is the second part of a three-part paper. In the first part. Civ. Engng Syst. 1 986, 3(3), 143-1 54 different models of uncertainties from the simplest interval representation, to fuzzy sets and random numbers are described. This paper discusses uncertain inference or reasoning based on evidence-hypothesis rules. The rules are expressed in algorithmic, functional form. The process of uncertainty propagation, i.e. from uncertainties in the evidence to the uncertainty in the hypothesis, is discussed with reference to several kinds of uncertainty representations described in part 1. When a hypothesis is supported by several rules with differing uncertainties, ways to aggregate these uncertainties are described. As in part 1, the discussion emphasizes the commonality and differences among the various uncertainty representations and. in particular, theircomputational ramifications on inference within the narrow context of the rule-framework considered.