Kutato: An Entropy-Driven System for Construction of Probabilistic Expert Systems from Databases
This work addresses the challenge of building expert systems from data for AI and data analysis applications, representing an incremental improvement in automated network construction.
The authors tackled the problem of automatically constructing probabilistic expert systems (belief networks) from databases by developing Kutato, an entropy-driven system that iteratively adds arcs to minimize network entropy. The system consistently reproduced original belief networks with high fidelity when tested on generated databases.
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.