Tools for detecting dependencies in AI systems
Matthew D. Schmill, Tim Oates, Paul R. Cohen · 2002
Presents a methodology for learning complex dependencies in data based on streams of categorical time-series data. The streams representation is applicable in a variety of situations. A program's execution trace may be thought of as a stream. The various monitor readings of an intensive care unit may be thought of as concurrent streams. Our learning methodology, called 'dependency detection', examines one or more streams to characterize a recurring structure with a set of dependency rules. These dependency rules are useful not only as a description of how the data is structured, but as a means for predicting future stream states. Further, we describe a set of tools for program analysis that use dependency detection.