Delta learning system for using expert advice to revise diagnostic

Abstract

A delta learning system takes as an initial fault hierarchy (KB.sub.0) and a set of annotated session transcripts, and is given a specified set of revision operators where each operator within a group maps a fault hierarchy (KB) to a slightly different or revised fault hierarchy (.theta..sub.i (KB)). The revised fault hierarchy (.theta..sub.i (KB)) is called a neighbor of the fault hierarchy (KB), and a set of all neighbors (N(KB)) is considered the fault hierarchy neighborhood. The system uses the revision operators to hill climb from the initial fault hierarchy (KB.sub.0), through successive hierarchies (KB.sub.1 . . . KB.sub.m), with successively higher empirical accuracies over the annotated session transcripts. The final hierarchy (KB.sub.m), is a local optimum in the space defined by the revision operators. At each stage, to go from a fault hierarchy (KB.sub.i) to its neighbor (KN.sub.i+1), the accuracy of the fault hierarchy (KB.sub.i) is evaluated over the annotated session transcripts, and the accuracy of each fault hierarchy (KB*) belonging to the set of all neighbors (N(KB.sub.i)) is also evaluated. If any fault hierarchy (KB*) is found to be more accurate than the fault hierarchy (KB.sub.i), then this fault hierarchy (KB*) becomes the new standard labeled KB.sub.i+1.