1. A computer-implemented method for artificial intelligence (AI) planning based quasi-Monte Carlo simulation for probabilistic planning, comprising: using a computer processor, storing into a computer memory an initial state of a system and a description of a target domain; 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 by an AI planner a set of sample future outcomes for the initial state; generating by a quasi-Monte Carlo simulation module probabilities of solutions for each of the sample future outcomes; evaluating future outcome solutions that are either highest probability, or lowest probability and highest-impact, relative to the solutions generated by the AI planner, wherein the AI planner searches a probabilistic planning tree for harmful sequences of actions which are either highest probability, or lowest probability and highest-impact, relative to the solutions generated by the AI planner for focused evaluation thereof; aggregating the evaluated solutions with future outcome solutions generated by the quasi-Monte Carlo simulation module, each of the aggregated solutions indicating a state of the system after a corresponding outcome occurs; and analyzing the aggregated set of future outcome solutions; automatically 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 for probabilistic planning.