Uncertainty in Artificial Intelligence
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Kutató: An Entropy-Driven System for Construction of Probabilistic Expert Systems from Databases
Edward Herskovits, Gregory Cooper
Abstract:
Kutató 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. Kutató 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 Kutató. The system consistently reproduces the original belief networks with high fidelity.
Keywords: null
Pages: 117-125
PS Link:
PDF Link: /papers/90/p117-herskovits.pdf
BibTex:
@INPROCEEDINGS{Herskovits90,
AUTHOR = "Edward Herskovits and Gregory Cooper",
TITLE = "Kutató: An Entropy-Driven System for Construction of Probabilistic Expert Systems from Databases",
BOOKTITLE = "Uncertainty in Artificial Intelligence 6 Annual Conference on Uncertainty in Artificial Intelligence (UAI-90)",
PUBLISHER = "Elsevier Science",
ADDRESS = "Amsterdam, NL",
YEAR = "1990",
PAGES = "117--125"
}


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