Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

OntoPret: An Ontology for the Interpretation of Human Behavior

Alexis Ellis, Stacie Severyn, Fjollë Novakazi, Hadi Banaee, Cogan Shimizu

TL;DR

The paper addresses the challenge of machines interpreting complex human behavior in collaborative settings, bridging the gap between techno-centric robotic frameworks and descriptive behavioral ontologies. It introduces OntoPret, a modular ontology grounded in cognitive science that comprises Scenario, Expectation, and Behavior modules to support real-time interpretation of deviations and deception, with formal axioms linking observations to interpretations and Theory of Mind-based reasoning. It leverages the MOMO methodology and design patterns, and demonstrates applicability through manufacturing and gameplay use cases, highlighting real-time intention recognition capabilities. The work contributes a formal, machine-processable foundation for human–machine collaboration that is extensible to multiple domains and promises safer, more resilient hybrid environments in Industry 5.0. Its significance lies in enabling context-aware interpretation of human actions to inform adaptive responses, task planning, and trust in human–machine teams.

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.

OntoPret: An Ontology for the Interpretation of Human Behavior

TL;DR

The paper addresses the challenge of machines interpreting complex human behavior in collaborative settings, bridging the gap between techno-centric robotic frameworks and descriptive behavioral ontologies. It introduces OntoPret, a modular ontology grounded in cognitive science that comprises Scenario, Expectation, and Behavior modules to support real-time interpretation of deviations and deception, with formal axioms linking observations to interpretations and Theory of Mind-based reasoning. It leverages the MOMO methodology and design patterns, and demonstrates applicability through manufacturing and gameplay use cases, highlighting real-time intention recognition capabilities. The work contributes a formal, machine-processable foundation for human–machine collaboration that is extensible to multiple domains and promises safer, more resilient hybrid environments in Industry 5.0. Its significance lies in enabling context-aware interpretation of human actions to inform adaptive responses, task planning, and trust in human–machine teams.

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.

Paper Structure

This paper contains 19 sections, 3 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Aim and Contributions
  3. Foundational Concepts
  4. Core Foundational Concepts
  5. Deviation
  6. Deception
  7. Cognitive Foundations for Behavior Interpretation
  8. Human Error Classification
  9. Ontology Design Methodology
  10. Ontology Modeling Approach
  11. Application Context
  12. An Ontology for Interpreting Behavior
  13. The Scenario Module
  14. The Expectation Module
  15. The Behavior Module
  16. ...and 4 more sections

Figures (3)

  • Figure 1: Simple representations of Deception and Deviation
  • Figure 2: Schema diagram for OntoPret. Gold boxes are classes, closed arrows indicate object properties, and open arrows indicate subsumption in the direction of the arrow. Grouped boxes are disjoint subclasses.
  • Figure 3: This figure shows OntoPret, but organized into three overarching conceptual components or modules: (1) the Scenario Module, (2) the Expectation Module, and (3) the Behavior Module.