Balance Preferences with Performance in Group Role Assignment.

Area of Science:

• Computer Science
• Artificial Intelligence
• Operations Research

Background:

• Role assignment is crucial for effective role-based collaboration.
• Decision-making in role assignment considers factors like preferences and team performance.
• Existing methods may lack efficiency or flexibility in handling these factors.

Purpose of the Study:

• To propose novel methods for solving the group role assignment with balance problem.
• To integrate decision-maker preferences and team performance into a unified approach.
• To leverage association rule mining for efficient and adaptable role assignment.

Main Methods:

• Developed a series of methods for group role assignment based on decision-maker preferences and team performance.
• Utilized the 'one clause at a time' association rule mining approach.
• Employed simulation experiments for verification and validation.

Main Results:

• The proposed methods effectively solve the group role assignment with balance problem.
• Simulation experiments confirmed the practicability and effectiveness of the solutions.
• Decision-makers can establish coarse-grain preferences, with fine-grain preferences being mined automatically.

Conclusions:

• The developed methods offer a practical and efficient solution for group role assignment.
• A balance is achieved between fine-grain preferences and overall team performance.
• This approach enhances the decision-making process in collaborative environments.