Adaptive Collaboration Based on the E-CARGO Model

摘要

Adaptive Collaboration (AC) is essential for maintaining optimal team performance on collaborative tasks. However, little research has discussed AC in multi-agent systems. This paper introduces AC within the context of solving real-world team performance problems using computer-based algorithms. Based on the authors’ previous work on the Environment-Class, Agent, Role, Group, and Object (E-CARGO) model, a theoretical foundation for AC using a simplified model of role-based collaboration (RBC) is proposed. Several parameters that affect team performance are defined and integrated into a theorem, which showed that dynamic role assignment yields better performance than static role assignment. The benefits of implementing AC are further proven by simulating a “future battlefield” of remotely-controlled robotic vehicles; in this scenario, team performance clearly benefits from shifting vehicles (or roles) using a single controller. Related research is also discussed for future studies.