OntoPret: An Ontology for the Interpretation of Human Behavior
Alexis Ellis, Stacie Severyn, Fjoll\"e Novakazi, Hadi Banaee, and Cogan Shimizu

TL;DR
OntoPret is a formal ontology designed to enable machines to interpret complex human behaviors in real-time, supporting applications in manufacturing and gaming through reasoning about intentions and actions.
Contribution
This paper introduces OntoPret, a novel, modular ontology grounded in cognitive science for real-time interpretation of human behaviors in human-machine teaming.
Findings
Successfully applied in manufacturing and gameplay scenarios
Provides a formal framework for classifying behaviors including deviations and deception
Enables advanced reasoning about human intentions
Abstract
As human machine teaming becomes central to paradigms like Industry 5.0, a critical need arises for machines to safely and effectively interpret complex human behaviors. A research gap currently exists between techno centric robotic frameworks, which often lack nuanced models of human behavior, and descriptive behavioral ontologies, which are not designed for real time, collaborative interpretation. This paper addresses this gap by presenting OntoPret, an ontology for the interpretation of human behavior. Grounded in cognitive science and a modular engineering methodology, OntoPret provides a formal, machine processable framework for classifying behaviors, including task deviations and deceptive actions. We demonstrate its adaptability across two distinct use cases manufacturing and gameplay and establish the semantic foundations necessary for advanced reasoning about human intentions.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
