Evidence Reliability in Nonmonotonic Domains and Nearly Monotonic Problems

Many AI domains are nonmonotonic. The resulting uncertainty is often dealt with by associating degree-of-belief ratings (conditional probabilities) with propositions. However, in some domains, a key issue is determining the subset of the available data that the agent should process to use as the basis for making a decision about how to act. Since belief ratings provide no sense of their \re-liability" (if additional data were considered), they do not help an agent determine when suucient data has been considered. We believe that in many domains, there is a strong correlation between current belief and ultimate belief once some fraction of the available data has been processed. We call such problems nearly monotonic, and suggest that they are important in that they can support eecient satisscing problem solving. The content of the information does not necessarily reeect the position or the policy of the Government, and no oocial endorsement should be inferred.