OntoPret: An Ontology for the Interpretation of Human Behavior

2025-10-27

AI Summary

Existing human–robot collaboration systems struggle to interpret complex human behaviors—such as task deviation and deception—in real time due to the absence of rigorous human behavioral modeling and the inability of descriptive ontologies to support efficient, runtime reasoning. Method: This paper introduces the first behavior interpretation ontology framework that integrates cognitive science principles with modular engineering design. It overcomes dual limitations: (i) technology-centric robot architectures lacking human behavior modeling capabilities, and (ii) static ontologies unsuitable for real-time semantic inference. The framework establishes a machine-processable, semantically tractable, and architecturally extensible behavior taxonomy, enabled by formal ontology modeling, lightweight semantic reasoning, and modular system design. Contribution/Results: Evaluated in heterogeneous domains—industrial manufacturing and interactive gaming—the framework achieves significant improvements in behavior recognition accuracy and collaborative safety. It provides a deployable theoretical foundation and technical infrastructure for trustworthy human–machine coexistence in Industry 5.0.

Technology Category

• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Intelligent Robots: Human-Robot Interaction

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

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.

Problem

• Bridging techno-centric robotics and descriptive behavioral ontologies gap
• Providing real-time collaborative interpretation of human behaviors
• Classifying task deviations and deceptive actions computationally

Innovation

• Ontology-based framework for human behavior interpretation
• Formal machine-processable classification of behavioral patterns
• Modular engineering methodology for cross-domain adaptability