Probabilistic planning with clear preferences on missing information Original Research Article

For many real-world problems, environments at the time of planning are only partially-known. For example, robots often have to navigate partially-known terrains, planes often have to be scheduled under changing weather conditions, and car route-finders often have to figure out paths with only partial knowledge of traffic congestions. While general decision-theoretic planning that takes into account the uncertainty about the environment is hard to scale to large problems, many such problems exhibit a special property: one can clearly identify beforehand the best (called clearly preferred) values for the variables that represent the unknowns in the environment. For example, in the robot navigation problem, it is always preferred to find out that an initially unknown location is traversable rather than not, in the plane scheduling problem, it is always preferred for the weather to remain a good flying weather, and in route-finding problem, it is always preferred for the road of interest to be clear of traffic. It turns out that the existence of the clear preferences can be used to construct an efficient planner, called PPCP (Probabilistic Planning with Clear Preferences), that solves these planning problems by running a series of deterministic low-dimensional A*-like searches.