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Objects in the world usually appear in context, participating in spatialrelationships and interactions that are predictable and expected. Knowledge ofthese contexts can be used in the task of using a mobile camera to search for aspecified object in a room. We call this the object search task. This paper isconcerned with representing this knowledge in a manner facilitating itsapplication to object search while at the same time lending itself toautonomous learning by a robot. The ability for the robot to learn suchknowledge without supervision is crucial due to the vast number of possiblerelationships that can exist for any given set of objects. Moreover, since arobot will not have an infinite amount of time to learn, it must be able todetermine an order in which to look for possible relationships so as tomaximize 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 arelikely to convey the most new information and should be examined first. Thispaper demonstrates how a representation based on statistical confidenceintervals allows the construction of a system that achieves the above goals. Analgorithm, based on the Highest Impact First heuristic, is presented as a meansfor providing a "focus of interest" with which to control the learning process,and examples are given.

Application of Confidence Intervals to the Autonomous Acquisition of High-level Spatial Knowledge