Abstract: A computer-based method and system for AI planning based quasi-Monte Carlo simulation for probabilistic planning are provided. The method includes generating a set of possible actions for an initial state, generating a set of sample future outcomes, generating solutions for each of the sample future outcomes, using an AI planner, generating a set of future outcome solutions that are low probability and high-impact, combining the solutions generated from each of the sample future outcomes with the future outcome solutions generated by the AI Planner into an aggregated set of future outcome solutions, analyzing the aggregated set of future outcome solutions, selecting a best action based at least partially on the analysis of the aggregated set of future outcome solutions, and outputting the selected best action to computer memory.