This paper investigates two different activities that involve making assumptions: predicting what one expects to be true and explaining observations. In a companion paper, a logic-based architecture for both prediction and explanation is proposed and an implementation is outlined. In this paper, we show how such a hypothetical reasoning system can be used to solve recognition, diagnostic and prediction problems. As part of this is the assumption that the default reasoner must be ``programmed'' to get the right answer and it is not just a matter of ``stating what is true'' and hoping the system will magically find the right answer. A number of distinctions have been found in practice to be important: between predicting whether something is expected to be true versus explaining why it is true; and between conventional defaults (assumptions as a communication convention), normality defaults (assumed for expediency) and conjectures (assumed only if there is evidence). The effects of these distinctions on recognition and prediction problems are presented. Examples from a running system are given.
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