Unsupervised Novelty Characterization in Physical Environments Using Qualitative Spatial Relations

Detecting, characterizing and adapting to novelty, whether in the form of previously unseen objects or phenomena, or unexpected changes in the behavior of known elements, is essential for Artificial Intelligence agents to operate reliably in unconstrained real-world environments.

We propose an automatic, unsupervised approach to novelty characterization for dynamic domains, based on describing the behaviors and interactions of objects in terms of their possible actions. To abstract from the variety of realizations of an action that can occur in physical domains, we model states in terms of qualitative spatial relations (QSRs) between their entities.

By first learning a model of actions in the non-novel environment from the state transitions observed as the agent interacts with the world, we can detect novelty by the persistent deviations from this model that it causes, and characterize the novelty by new or modified actions.

We also present a new method of learning action models from observation, based on conceptual similarity and hierarchical clustering.