Document Type
Conference Proceeding
Abstract
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.
Publication Info
24th International Conference on Autonomous Agents and Multiagent Systems, 2025.
APA Citation
Shyalika, C., Prasad, R., Jaimini, U., Henson, C., El Kalach, F., & Sheth, A. (2025). Multiagent CoPilot in Industrial AI Applications. Proceedings of the 24th International Conference on Autonomous Agents and Multiagent Systems (AAMAS-25).
Rights
© 2025 International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). This work is licenced under the Creative Commons Attribution 4.0 International (CC-BY 4.0) licence.