Abstract
Human-AI collaboration in industrial manufacturing promises to overcome current limitations by combining the flexibility of human intelligence and the scaling and processing capabilities of machine intelligence. To ensure effective collaboration between human and AI team members, we envision a software-driven coordination mechanism that orchestrates the interactions between the participants in Human-AI teaming scenarios and help to synchronize the information flow between them. A structured process-oriented approach to systems engineering aims at generalizability, deployment efficiency and enhancing the quality of the resulting software by formalizing the human-AI interaction as a BPMN process model. During runtime, this process model is executed by the teaming engine, one of the core components of the Teaming.AI software platform. By incorporating dynamic execution traces of these process models into a knowledge graph structure and linking them to contextual background knowledge, we facilitate the monitoring of variations in process executions and inference of new insights during runtime. Knowledge graphs are a powerful tool for semantic integration of diverse data, thereby significantly improving the data quality, which is still one of the biggest issues in AI-driven software solutions. We present the Teaming.AI software platform and its key components as a framework for enabling transparent teamwork between humans and AI in industry. We discuss its application in the context of an industrial use case in plastic injection molding production. Overall, this Teaming.AI platform provides a robust, flexible and accountable solution for human-AI collaboration in manufacturing.
Original language | English |
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Title of host publication | Software Quality as a Foundation for Security - 16th International Conference on Software Quality, SWQD 2024, Proceedings |
Editors | Peter Bludau, Rudolf Ramler, Dietmar Winkler, Johannes Bergsmann |
Publisher | Springer Science and Business Media Deutschland GmbH |
Pages | 76-87 |
Number of pages | 12 |
ISBN (Print) | 9783031562808 |
DOIs | |
Publication status | Published - 2024 |
Event | 16th International Conference on Software Quality, SWQD 2024 - Vienna, Austria Duration: 23 Apr 2024 → 25 Apr 2024 |
Publication series
Series | Lecture Notes in Business Information Processing |
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Volume | 505 LNBIP |
ISSN | 1865-1348 |
Conference
Conference | 16th International Conference on Software Quality, SWQD 2024 |
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Country/Territory | Austria |
City | Vienna |
Period | 23/04/24 → 25/04/24 |
Bibliographical note
Publisher Copyright:© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Keywords
- Human-AI Collaboration
- Industrial Knowledge Graph
- Plastic Injection Molding
- Process Orchestration