Adaptive Collaboration Systems: Self-Sustaining Systems for Optimal Performance

Abstract

Adaptability is a common and typical property for natural systems in the real world. It is also an important and desirable property for computer supported artificial systems. An adaptive collaboration system (ACS) can be viewed as a set of interacting intelligent agents, real or abstract, forming an integrated system that can respond to internal and environmental changes. Feedback is a key feature of such systems because it enables appropriate responses to change. Artificial systems can be made adaptive by using feedback to sense new conditions in the environment and then adjusting accordingly. ACSs can find applications in almost all industrial sectors, particularly in aerospace, automotive, manufacturing, and management. Adaptive collaboration (AC) can be realized through the promising architecture and process of role-based collaboration (RBC) [21]. RBC is a computational methodology that uses roles [21] as primary underlying mechanisms to facilitate collaboration. RBC has been developed into a methodology of discovery in the research of collaboration systems, because it takes advantage of formalizations and abstractions of system components through mathematical expressions. Problem instances of such abstractions are easily found in real-world scenarios.