A Methodology for Using a Default and Abductive Reasoning System

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.