Workshop on Distributed AI Architectures and Enabling Technologies for Intelligent and Adaptive Industrial Processes (IND-DAI)

Scope

Current industrial processes are evolving towards scenarios characterized by high operational complexity, system heterogeneity, and the need for continuous adaptation to changing environmental conditions, workload dynamics, and the internal state of assets. In this context, the application of artificial intelligence can no longer be addressed solely through centralized or isolated approaches, but instead requires distributed architectures capable of integrating perception, analysis, and decision-making across multiple levels of the system.

This Special Session focuses on the study of solutions based on distributed artificial intelligence and emerging technologies applied to industrial processes, with particular attention to approaches that enable early anomaly detection, dynamic system adaptation, and efficient coordination of multiple heterogeneous components and agents. The session addresses architectures in which intelligence is deployed in a hierarchical and cooperative manner across sensors, embedded systems, edge computing nodes, and cloud platforms, while explicitly considering real-world constraints such as latency, energy consumption, reliability, and cybersecurity.

Contributions are sought that rigorously analyze how the combination of machine learning techniques, advanced perception, edge computing, digital twins, and distributed learning mechanisms can enhance the operation, supervision, and optimization of complex industrial processes. Particular interest is given to works presenting experimental validation, pilot deployments, or industrial case studies in which artificial intelligence operates as an integral part of the process itself, rather than as an external supervisory layer.

The session welcomes original research papers, methodological and architectural proposals, experimental studies, industrial use cases, technological demonstrators, research project summaries, and PhD work related to the rigorous application of artificial intelligence and emerging technologies in industrial processes.