Tools for detecting dependencies in AI systems

Matthew D. Schmill, Tim Oates, Paul R. Cohen

Research output: Contribution to journalConference article

Abstract

We present 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 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.

Original languageEnglish (US)
Pages (from-to)148-155
Number of pages8
JournalProceedings of the International Conference on Tools with Artificial Intelligence
StatePublished - Dec 1 1995
EventProceedings of the 1995 IEEE 7th International Conference on Tools with Artificial Intelligence - Herndon, VA, USA
Duration: Nov 5 1995Nov 8 1995

ASJC Scopus subject areas

  • Software

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