1. A computer-implemented method for AI planning based quasi-Monte Carlo simulation for probabilistic planning, comprising: using a computer processor, receiving an initial state and a description of a target domain into computer memory; generating a set of possible actions for the initial state; for each action in the set of the possible actions, performing a sequence of actions, comprising: 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; aggregating the solutions generated by the AI planner with the sample future outcomes; and 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.