Probabilistic Reasoning for Plan Robustness

A planning system must reason about the uncer-tainty of continuous variables in order to accurately project the possible system state over time. A method is devised for directly reasoning about the uncertainty in continuous activity duration and re-source usage for planning problems. By represent-ing random variables as parametric distributions, computing projected system state can be simplified in some cases. Common approximation and novel methods are compared for over-constrained and lightly constrained domains. The system compares a few common approximation methods for an iter-ative repair planner. Results show improvements in robustness over the conventional non-probabilistic representation by reducing the number of constraint violations witnessed by execution. The improve-ment is more significant for larger problems and problems with higher resource subscription levels but diminishes as the system is allowed to accept higher risk levels.