Group Role Three-Way Assignment for Managing Uncertainty in Role Negotiation

Area of Science:

• Computer Science
• Artificial Intelligence
• Operations Research

Background:

• Role-based collaboration (RBC) aims to improve teamwork.
• Role negotiation (RN) determines agent roles and numbers, but faces challenges due to dynamic task volumes.
• Existing RBC models often assume static role requirements, limiting their effectiveness.

Purpose of the Study:

• To address uncertainties in role requirements within RBC.
• To introduce a novel Group Role Three-Way Assignment (GR3A) model.
• To enhance group performance by managing dynamic agent needs.

Main Methods:

• Quantifying role requirements using truncated discrete distribution.
• Developing a Group Role Three-Way Assignment (GR3A) model incorporating uncertainty.
• Proposing a linearization strategy for the dual-objective nonlinear optimization problem.
• Defining sufficient and necessary conditions for assignment efficacy.

Main Results:

• The GR3A model effectively manages dynamic agent requirements in RBC.
• The model includes a partial substitute option for agents during non-engagement periods.
• Simulations demonstrate the effectiveness and efficacy of the proposed GR3A method.
• The study innovatively integrates truncated discrete distribution and Three-Way Decision (3WD) into RBC.

Conclusions:

• The proposed GR3A model offers a significant advancement over traditional RBC approaches.
• This framework provides a robust solution for optimizing group performance under uncertain conditions.
• The integration of 3WD and truncated discrete distribution enhances the adaptability of RBC systems.