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 […]