Plan Development using Local Probabilistic Models
  Approximate models of world state transitions are necessary when building
plans for complex systems operating in dynamic environments. External event
probabilities can depend on state feature values as well as time spent in that
particular state. We assign temporally -dependent probability functions to
state transitions. These functions are used to locally compute state
probabilities, which are then used to select highly probable goal paths and
eliminate improbable states. This probabilistic model has been implemented in
the Cooperative Intelligent Real-time Control Architecture (CIRCA), which
combines an AI planner with a separate real-time system such that plans are
developed, scheduled, and executed with real-time guarantees. We present flight
simulation tests that demonstrate how our probabilistic model may improve CIRCA
performance.
