Most AI search/representation techniques are oriented toward an infinite domain of objects and arbitrary relations among them. In reality much of what needs to be represented in AI can be expressed using a finite domain and unary or binary predicates. Well-known vector- and matrix-based representations can efficiently represent finite domains and unary/binary predicates, and allow effective extraction of path information by generalized transitive closure/path matrix computations. In order to avoid space limitations in this approach, a set of abstract sparse matrix data types was developed along with a set of operations on them. This representation forms the basis of an intelligent information tool for representing and manipulating relational data. The tool is being used in developing a system that helps flight crews cope with in-flight malfunctions.
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