Learning from Incongruence.

We present an approach to constructing a model of the universe for explaining observations and making decisions based on learning new concepts. We use a weak statistical model, e.g. a discriminative classifier, to distinguish errors in measurements from improper modeling. We use boolean logic to combine outcomes of direct detectors of relevant events, e.g. presence of sound and presence of human shape in the field of view, into more complex models explaining the states in which the universe may appear. The process of constructing a new concept is initiated when a significant disagreement incongruence has been observed between incoming data and the current model of the universe. Then, a new concept, i.e. a new direct detector, is trained on incongruent data and combined with existing models to remove the incongruence. We demonstrate the concept in an experiment with human audio-visual detection.