Non-montonic logics have been used recently for a variety of A.I. purposes, including belief revision and default reasoning in question-answering and exper systems. This paper argues that by their nature, such systems dicard information which has a role in human belief systems. In particular, systems which use non-monotonic reasoning lose the distinction between fully justified inferences and reasonable presumptions, in the process losing the ability to record failed expectations as such, an ability which provides a useful measure of salience for A.I. systems.

This paper discusses default reasoning, distinguishing generalizations associated vdth defaults from both universals and statistical generalizations. I argue that conclusions based on defaults should be reported differently frcm conclusions which do not involve default reasoning, and that however we represent than, the related inference system must distinguish default claims from other propositions and treat than differently. Two existing analyses of default reascxung are briefly criticized in light of the distinctions presented.