E-CARGO/RBC: Enabling Research Innovations in the Era of AI

In the era of Artificial Intelligence, powerful tools such as Large Language Models (LLMs) can perform many low-level intelligent tasks. Thus, many routine jobs face a high risk of automation. To address these challenges, researchers must strengthen their ability for high-level modeling and system design.
E-CARGO/RBC (Environments–Classes, Agents, Roles, Groups, and Objects / Role-Based Collaboration) is a modeling methodology designed to support this shift. RBC is a computational methodology that uses E-CARGO as the fundamental mechanisms to facilitate collaboration. It provides a systematic framework for investigating collaboration and complex systems. Over the decades, E-CARGO/RBC have evolved into powerful tools for modeling, analyzing, designing, and managing systems in domains related to systems engineering such as systems and social intelligence.
E-CARGO enables researchers to formalize complex problems and make them solvable. Its effectiveness has been validated through significant problem models such as Group Role Assignment (GRA) and its extensions, which can be solved using optimization platforms. Moreover, E-CARGO is extensible and can be refined for specific domains, potentially leading to new modeling methodologies.
This talk reviews the foundations, achievements, and open challenges of RBC and E-CARGO, highlights key case studies such as GRA, and encourages further research, applications, and critical discussions.