A Theoretical Framework for Context-Sensitive Temporal Probability Model
  Construction with Application to Plan Projection
  We define a context-sensitive temporal probability logic for representing
classes of discrete-time temporal Bayesian networks. Context constraints allow
inference to be focused on only the relevant portions of the probabilistic
knowledge. We provide a declarative semantics for our language. We present a
Bayesian network construction algorithm whose generated networks give sound and
complete answers to queries. We use related concepts in logic programming to
justify our approach. We have implemented a Bayesian network construction
algorithm for a subset of the theory and demonstrate it's application to the
problem of evaluating the effectiveness of treatments for acute cardiac
conditions.
