SIFT researcher awarded patent
Dr. J. Benton has been awarded patent 8,473,447, "AI planning based quasi-monte carlo simulation method for probabilistic planning," with Dr. Sungwook Yoon, Dr. Minh Do, and Prof. Wheeler Ruml. This patent has a variety of applications including planning and scheduling in manufacturing, finding plans for mobile robots, and traffic management.
The patent covers algorithms that use quasi Monte Carlo methods to help decide what actions to take in the face of probabilistic effects. By simulating possible "what-if" plans that may result from executing possible "next actions," the algorithms can estimate the possible gain provided by each action. This patent includes several advantages over other Monte Carlo techniques, including accounting for rare action effects and caching of "what-if" simulations.
AI planning based quasi-monte carlo simulation method for probabilistic planning
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.