In this work a default revision mechanism is introduced into Speculative
Computation to manage incomplete information. The default revision
is supported by a method for the generation of default constraints based on
Bayesian Networks. The method enables the generation of an initial set of
defaults which is used to produce the most likely scenarios during the computation,
represented by active processes. As facts arrive, the Bayesian Network
is used to derive new defaults. The objective with such a new dynamic mechanism
is to keep the active processes coherent with arrived facts. This is achieved
by changing the initial set of default constraints during the reasoning process
in Speculative Computation. A practical example in clinical decision support
is described.info:eu-repo/semantics/publishedVersio