The AMPS algorithm promises to break these deadlocks, by allowing robots to reconsider the importance of various labels. “It’s more than just where things are, it’s what they are, what they’re composed of,” says How. For example, how crucial is it for a conference room to have chairs? And if one robot has already spotted what it considers a storage room, complete with boxes, cabinets and shelves, would there really be another storage room so close to it (without any of those tell-tale features)? According to How, who created the algorithm with his graduate student, Trevor Campbell, the trick is to allow the interfacing machines to establish new priorities for their labels, rebuilding their worldview. By allowing for conference rooms that may or may not have chairs in them, and reordering their labels to account for different experiences, the robots can achieve what How and Campbell refer to as semantic symmetry.