aHUGIN: A System Creating Adaptive Causal Probabilistic Networks
This work provides a tool for building adaptive systems in probabilistic modeling, but it appears incremental as it extends prior methods without claiming major breakthroughs.
The paper introduces aHUGIN, a tool for creating adaptive causal probabilistic networks that adjust conditional probabilities, based on an extension of the HUGIN shell and existing methods, with results discussed from experimental sessions.
The paper describes aHUGIN, a tool for creating adaptive systems. aHUGIN is an extension of the HUGIN shell, and is based on the methods reported by Spiegelhalter and Lauritzen (1990a). The adaptive systems resulting from aHUGIN are able to adjust the C011ditional probabilities in the model. A short analysis of the adaptation task is given and the features of aHUGIN are described. Finally a session with experiments is reported and the results are discussed.