Year: 2025
Type: preprint
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.
Source: arXiv (Cornell University)
Authors: Ellis, Alexis, Severyn, Stacie, Novakazi, Fjollë, Banaee, Hadi, Shimizu, Cogan
Institutions: Wright State University, Örebro University
Topic: Semantic Web and Ontologies
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7101934950
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