AAMAS 2025 Tutorial | Multiagent CoPilot in Industrial AI Applications
May 19-23, 2025 | Detroit, Michigan, USA
In the era of smart automation and digital transformation, achieving efficiency, precision, and adaptability is essential for industries to remain competitive. Sectors, including manufacturing, supply chain and logistics, healthcare, finance, and retail, face significant challenges in deploying Artificial Intelligence (AI) solutions tailored to their unique needs, particularly in critical, resource-constrained applications. According to Gartner’s 2024 Hype Cycle for Artificial Intelligence, composite AI, which integrates techniques like machine learning, knowledge graphs, and rule-based systems, is becoming foundational for industries, enhancing predictions, decisions, and scalability across complex environments. The complexity of real-world systems requires Industrial AI solutions to be customizable to business needs, compact for efficient deployment on resource-constrained devices, and agile to adapt to changing requirements. By being neurosymbolic, such solutions integrate data, knowledge, and human expertise to create robust, explainable, and trustworthy AI that supports planning and reasoning. In this tutorial, we will introduce Multiagent CoPilot for Industrial AI applications focusing on the primary use case of manufacturing (offering requirements, data, knowledge, human expertise). The use cases we will describe are inspired by collaborations with, or similar efforts at Bosch, Hewlett Packard Enterprise, Siemens, and others. AAMAS audience will learn about human-in-the-loop CoPilots as we explore how multiagent coordination, collaboration, and decision-making can enhance the functionality of industrial AI models. With our primary use case, we will demonstrate how to address the unique challenges faced by the manufacturing industry, from improving operational efficiency to enhancing adaptability in critical tasks. However, the knowledge and insights gained from this tutorial are applicable and generalizable to various industries, like transportation and healthcare, offering valuable perspectives for researchers and professionals across domains seeking to adopt these technologies in real-world applications.
The target audience includes academic researchers, data scientists and practitioners working in industrial AI, practitioners applying AI in industrial applications, particularly those who apply machine learning techniques in complex industry environments.
The tutorial is designed to provide participants with a well-balanced mix of theoretical insights and hands-on experience within a half-day session. The interactive format ensures active engagement and concludes with a dedicated Q&A session.
Kickstarting with an introduction to Industry 4.0, its evolving challenges, and the role of industrial AI solutions. Participants will gain foundational knowledge on neurosymbolic AI and the core objectives of the tutorial.
Exploring CoPilots as intelligent, task-aware assistants that extend beyond basic automation. Key attributes include:
The Multiagent CoPilot framework will be introduced, emphasizing:
A hands-on walkthrough for developing Multiagent CoPilots, addressing industry-specific problems, workflow integration, and domain knowledge infusion. The demonstration will showcase:
Integration with knowledge graphs and ontologies for a robust neurosymbolic AI system.
Understanding causality’s role in AI, covering:
Demonstrating how Multiagent CoPilots improve:
Discussing the challenges of deploying Multiagent CoPilots, covering:
Open floor for participant queries and discussions, ensuring clarity on key concepts.
Chathurangi Shyalika is a Ph.D. student at the AI Institute, University of South Carolina. Her research interests focus on Deep Learning, Multimodal-AI, Time Series Analysis, and Neurosymbolic-AI. Contact: jayakodc@email.sc.edu
Renjith Prasad is a Ph.D. student at the AI Institute, University of South Carolina. His research focuses on multimodal learning, large language models, and neurosymbolic AI. Contact: kaippilr@mailbox.sc.edu
Utkarshani Jaimini is a Ph.D. candidate at the AI Institute, University of South Carolina. Her research focuses on neurosymbolic AI and causal reasoning. Contact: ujaimini@email.sc.edu
Cory Henson is a Lead Research Scientist at Bosch Center for AI. His work focuses on knowledge representation, neurosymbolic AI, and causal AI applications in manufacturing. Contact: cory.henson@us.bosch.com
Fadi El Kalach is a Ph.D. student in Automotive Engineering at Clemson University. His research focuses on Smart Manufacturing and the utilization of sensor data for real-time decision-making mechanisms to increase the intelligence of manufacturing systems. Contact: felkala@clemson.edu
Professor Amit Sheth is a leading AI researcher and entrepreneur. His research includes neuro-symbolic AI for trustworthy and explainable AI systems. He is a fellow of IEEE, AAAI, ACM, and AAIA. Contact: amit@sc.edu