The Advanced Computational Methods Center, University of Georgia

A Nonmonotonic Inference Engine People are often forced by circumstances to make judgments based on incomplete information. These circumstances do not disappear when we augment our native reasoning ability with the use of knowledge bases and automated reasoning systems. It is therefore extremely important that our systems be able to assist us in this kind of reasoning. Frequently, the best conclusion that can be drawn from an incomplete set of facts about a situation are different from the best conclusion that can be drawn from a complete or nearly complete superset of the same facts. The set of conclusions we draw as our information increases does not simply change in one direction or monotonically by getting larger; it can also shrink as our previous best conclusions are rejected on the basis of new information.