Topological Order Planner for POMDPs

We call this a topological structure [Dai and Goldsmith, 2007; Over the past few years, point-based POMDP Bonet and Geffner, 2003; Abbad and Boustique, 2003] and solvers scaled up to produce approximate solutions say that a problem has much topological structure when the to mid-sized domains. However, to solve real world problem state space has many layers. These characteristics problems, solvers must exploit the structure of the are embodied in many real-world applications including assembly domain. In this paper we focus on the topological line optimization; network routing; or railway traffic structure of the problem, where the state space control. Consider the assembly of a car that consists in multiple contains layers of states. We present here the Topological steps: first the car moves to the engine installation; then Order Planner (TOP) that utilizes the topological the engine installation crew checks for malfunctions; thereafter structure of the domain to compute belief finishing the engine installation the car moves respectively space trajectories. TOP rapidly produces trajectories to the hood and the wheel stations. Each transition focused on the solveable regions of the belief from a station to another is preceded by a quality measurement space, thus reducing the number of redundant backups procedure that prevents car malfunctions.