  Kutato is a system that takes as input a database of cases and produces a
belief network that captures many of the dependence relations represented by
those data. This system incorporates a module for determining the entropy of a
belief network and a module for constructing belief networks based on entropy
calculations. Kutato constructs an initial belief network in which all
variables in the database are assumed to be marginally independent. The entropy
of this belief network is calculated, and that arc is added that minimizes the
entropy of the resulting belief network. Conditional probabilities for an arc
are obtained directly from the database. This process continues until an
entropy-based threshold is reached. We have tested the system by generating
databases from networks using the probabilistic logic-sampling method, and then
using those databases as input to Kutato. The system consistently reproduces
the original belief networks with high fidelity.
