  A "partial ordering" is a way to heuristically order a set of examples
(partial orderings are a set where, for certain pairs of elements, one precedes
the other). While these orderings may only be approximate, they can be useful
for guiding a search towards better regions of the data. To illustrate the
value of that technique, this paper presents iSNEAK, an incremental
human-in-the-loop AI problem solver. iSNEAK uses partial orderings and feedback
from humans to prune the space of options. Further, in experiments with a dozen
software models of increasing size and complexity (with up to 10,000
variables), iSNEAK only asked a handful of questions to return human-acceptable
solutions that outperformed the prior state-of-the-art. We propose the use of
partial orderings and tools like iSNEAK to solve the information overload
problem where human experts grow fatigued and make mistakes when they are asked
too many questions. iSNEAK mitigates the information overload problem since it
allows humans to explore complex problem spaces in far less time, with far less
effort.
