Abstract:
This paper studies reasoning for the purpose of uncovering hidden assignments of values to a finite set of variables based on data acquired incrementally over time. This reasoning task, referred to here as datadriven refinement, is found in tasks such as diagnosis and learning. An empirical study is undertaken to evaluate the utility of knowledge gained from observations in effectively solving refinement problems.