A mobile agent with the task to classify its sensor pattern has to cope with ambiguous information. Active recognition involves the observer in a search for discriminative evidence, e.g. by change of its viewpoint. This paper defines the recognition process as a sequential decision problem with the objective to disambiguate initial object hypotheses. Reinforcement learning provides then an efficient method to autonomously develop near-optimal decision strategies in terms of sensorimotor mappings.
Active recognition accumulates evidence collected from a multitude of sensor observations.
Active object recognition in parametric eigenspace - Borotschnig, Paletta et al.