OntoPret: An Ontology for the Interpretation of Human Behavior
This addresses the need for safe and effective human-machine teaming in contexts like Industry 5.0, bridging a gap between robotic frameworks and descriptive ontologies.
The paper tackles the problem of machines interpreting complex human behaviors by introducing OntoPret, an ontology that provides a formal framework for classifying behaviors like task deviations and deceptive actions, demonstrating its adaptability in manufacturing and gameplay use cases.
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.