Efiective Online Detection of Task-Independent Landmarks

One of the key problems in building adaptive autonomous agents is landmark detection. Landmarks can be used for efficient navigation as well as for developing hierarchical cognitive structures. Previous approaches to landmark detection often simply chose landmarks as the agent’s location after fixed intervals of time. Other approaches to landmark detection have focused on the reliability and ease of detection of landmarks. However, systems that use landmarks for hierarchy formations rely on a set of landmarks that provides a means for a concise and effective decomposition of the environment. We believe that such a decomposition is achieved most effectively by identifying transitions that partition the environment into relatively independent sub-regions. Using notions of surprise and consolidation via continued novelty, implemented by relatively simple statistics on the sensory inputs, we introduce an online landmark detection mechanism that reliably identifies landmarks that correspond to such transitions. Since the detected landmarks partition the environment into relatively independent subspaces, the resulting set of landmarks should be very useful for the formation of an online adaptive hierarchical problem decomposition enabling efficient hierarchical adaptation and cognition.