I don't know any particular algorithms. However, we had some discussions
about this in the decision support group at Aalborg University some time
ago. We concluded that in some cases - if the same decision sub tree is
repeated on the different branches - it would be favourable to convert
it to a Bayesian network or influence diagram. Otherwise, if the
branches of the decision tree has very different structure, the best
representation would probably be the decision tree.
If there are only a few irregularities in the decision tree, you can do
some tricks to get around it when converting to a Bayesian network
(well, you also can if there are many, but the Bayesian network would be
ugly).
In these in-between cases, it might be interesting to be able to combine
the two modelling techniques. I don't know if anybody has tried to do
that!?
Lars
-- Lars Moltsen Nielsen HUGIN Expert A/S Email: ln@hugin.dk Niels Jernes Vej 10 Phone (o): +45 96 35 45 48 DK-9220 Aalborg East Phone (h): +45 98 13 13 56 Denmark