In this work, we present a generic framework for representing events and rules with uncertainty. We present a mechanism to construct the probability space that captures the semantics and defines the probabilities of possible worlds using an abstraction based on a Bayesian network. In order to improve derivation efficiency we employ two mechanisms: The first mechanism, which we term selectability, limits the scope of impact of events to only those rules to which they are relevant, and enables a more efficient calculation of the exact probability space. The second mechanism we employ is one of approximating the probability space by employing a sampling technique over a set of rules.
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