Abstract
Adaptive Collaboration (AC) is essential for group performance optimization in collaborative systems. This paper begins by introducing AC within the context of solving a real world problem. Next, AC problems are formalized based on the Environment-Class, Agent, Role, Group, and Object (E-CARGO) model. Three algorithms are then proposed for solving AC problems. These algorithms are based on three different scenarios: the current group state, the group state after a definite period of time and the group state all over the collaboration, respectively. Experiments are developed to analyze the performance of each proposed algorithm. Results indicate that the proposed algorithms perform better than static collaboration.
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