Application of Confidence Intervals to the Autonomous Acquisition of
  High-level Spatial Knowledge
  Objects in the world usually appear in context, participating in spatial
relationships and interactions that are predictable and expected. Knowledge of
these contexts can be used in the task of using a mobile camera to search for a
specified object in a room. We call this the object search task. This paper is
concerned with representing this knowledge in a manner facilitating its
application to object search while at the same time lending itself to
autonomous learning by a robot. The ability for the robot to learn such
knowledge without supervision is crucial due to the vast number of possible
relationships that can exist for any given set of objects. Moreover, since a
robot will not have an infinite amount of time to learn, it must be able to
determine an order in which to look for possible relationships so as to
maximize the rate at which new knowledge is gained. In effect, there must be a
"focus of interest" operator that allows the robot to choose which examples are
likely to convey the most new information and should be examined first. This
paper demonstrates how a representation based on statistical confidence
intervals allows the construction of a system that achieves the above goals. An
algorithm, based on the Highest Impact First heuristic, is presented as a means
for providing a "focus of interest" with which to control the learning process,
and examples are given.
