Understanding in Incomplete Worlds

Abstract

Most real world domains differ from the micro-worlds traditionally used in A.I. in that they have an incomplete factual database which changes over time. Understanding in these domains can be thought of as the generation of plausible inferences which are able to use the facts available, and respond to changes in them. A traditional rule interpreter such as Planner can be extended to construct plausible inferences in these domains by A) allowing assumptions to be made in applying rules, resulting in simplifications of rules which can be used in an incomplete database; B) monitoring the antecedents and consequents of a rule so that inferences can be maintained over a changing database. The resulting chains of inference can provide a dynamic description of an event. This allows general reasoning processes to be used to understand in domains for which large numbers of Schema-like templates have been proposed as the best model.