Qualitative Propagation and Scenario-based Explanation of Probabilistic
  Reasoning
  Comprehensible explanations of probabilistic reasoning are a prerequisite for
wider acceptance of Bayesian methods in expert systems and decision support
systems. A study of human reasoning under uncertainty suggests two different
strategies for explaining probabilistic reasoning: The first, qualitative
belief propagation, traces the qualitative effect of evidence through a belief
network from one variable to the next. This propagation algorithm is an
alternative to the graph reduction algorithms of Wellman (1988) for inference
in qualitative probabilistic networks. It is based on a qualitative analysis of
intercausal reasoning, which is a generalization of Pearl's "explaining away",
and an alternative to Wellman's definition of qualitative synergy. The other,
Scenario-based reasoning, involves the generation of alternative causal
"stories" accounting for the evidence. Comparing a few of the most probable
scenarios provides an approximate way to explain the results of probabilistic
reasoning. Both schemes employ causal as well as probabilistic knowledge.
Probabilities may be presented as phrases and/or numbers. Users can control the
style, abstraction and completeness of explanations.
