Knowledge of the Environment
We must distinguish between knowledge an agent receives through it's sensors and knowledge about the world from which the input comes. Knowledge about the world can be programmed in, and/or it can be learned through the sensor input. For example, a chess playing agent would be programmed with the positio ns of the pieces at the start of a game, but would maintain a representation of the entire board by updating it with every move it is told about through the input it receives. Note that the sensor inputs are the opponent's moves and this is different to the knowledge of the world that the agent maintains, which is the board state.
There are three main ways in which an agent can use knowledge of its world to inform its actions. If an agent maintains a representation of the world, then it can use this information to decide how to act at any given time. Furthermore, if it stores its representations of the world, then it can also use information about previous world states in its program. Finally, it can use knowledge about how its actions affect the world.
The RHINO agent was provided with an accurate metric map of the museum and exhibits beforehand, carefully mapped out by the programmers. Having said this, the layout of the museum changed frequently as routes became blocked and chairs were moved. By updating it's knowledge of the environment, however, RHINO consistently knew where it was, to an accuracy better than 15cm. RHINO didn't move objects other than itself around the museum. However, as it moved around, people followed it, so its actions really were altering the environment. It was because of this (and other reasons) that the designers of RHINO made sure it updated its plan as it moved around.