Up until now it has been assumed everywhere that the current state is known. What if the state is not known? In this case, information regarding the state is obtained from sensors during the execution of a plan. This situation arises in most applications that involve interaction with the physical world. For example, in robotics it is virtually impossible for a robot to precisely know its state, except in some limited cases. What should be done if there is limited information regarding the state? A classical approach is to take all of the information available and try to estimate the state. In robotics, the state may include both the map of its environment and the robot configuration. If the estimates are sufficiently reliable, then we may safely pretend that there is no uncertainty in state information. This enables many of the planning methods introduced so far to be applied with little or no adaptation.

The more interesting case occurs when state estimation is altogether avoided. It may be surprising, but many important tasks can be defined and solved without ever requiring that specific states are sensed, even though a state space is defined for the planning problem. To achieve this, the planning problem will be expressed in terms of an information space. Information spaces serve the same purpose for sensing problems as the configuration spaces of Chapter 4 did for problems that involve geometric transformations. Each information space represents the place where a problem that involves sensing uncertainty naturally lives.