KR2021Proceedings of the 18th International Conference on Principles of Knowledge Representation and ReasoningProceedings of the 18th International Conference on Principles of Knowledge Representation and Reasoning

Online event. November 3-12, 2021.

Edited by

ISSN: 2334-1033
ISBN: 978-1-956792-99-7

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Published by

Copyright © 2021 International Joint Conferences on Artificial Intelligence Organization

Unsupervised Novelty Characterization in Physical Environments Using Qualitative Spatial Relations

  1. Ruiqi Li(Australian National University)
  2. Hua Hua(Australian National University)
  3. Patrik Haslum(Australian National University)
  4. Jochen Renz(Australian National University)


  1. Reasoning about actions and change, action languages
  2. Qualitative reasoning, reasoning about physical systems


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.