Probabilistic Reasoning for Plan Robustness

Abstract

A planning system must reason about the uncertainty 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 resource usage for planning problems. By representing random variables as parametric distributions, computing projected system state can be simplified. Common approximations and novel methods are compared for over-constrained and lightly constrained domains within an iterative repair planner. Results show improvements in robustness over the conventional non-probabilistic representation by reducing the number of constraint violations during execution. The improvement is more significant for larger problems and those with higher resource subscription levels but diminishes as the system is allowed to accept higher risk levels.