Automatic contingency planning
Planning algorithms are widely used in logistics and control. They can help schedule flights and bus routes, guide autonomous robots, and determine control policies for the power grid, among other things. In recent years, planning algorithms have begun to factor in uncertainty — variations in travel time, erratic communication between autonomous robots, imperfect sensor data, and the like. That causes the scale of the planning problem to grow exponentially, but researchers have found clever ways to solve it efficiently. Now, researchers at MIT and the Australian National University (ANU) have made the problem even more complex, by developing a planning algorithm that also generates contingency plans, should the initial plan prove too risky. It also identifies the conditions — say, sensor readings or delays incurred — that should trigger a switch to a particular contingency plan. Despite the extra labor imposed by generating contingency plans, the algorithm still provides mathematical guarantees that its...