MACK: A Tool for Acquiring Consistent Knowledge Under Uncertainty

Eugene Santos Jr., Darwyn O. Banks., and Sheila B. Banks

We develop a new methodology and tool for knowledge acquisition under uncertainty. A new knowledge representation called Bayesian Knowledge Bases provides a powerful key to our approach and is well-grounded in probability theory. In this paper, we demonstrate the ease and flexibility with which knowledge acquisition can be accomplished while ensuring the consistency of the knowledge base as data is both acquired and subsequently maintained. We present the MACK tool and apply it to NASA’s Post-Test Diagnostics System which locates anomalies aboard the Space Shuttles’ Main Engines.


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