Transparent Task Delegation in Multi-Agent Systems Using the QuAD-V Framework
Jeferson José Baqueta, Mariela Morveli-Espinoza, César Augusto Tacla · Applied Sciences · 2025
Task delegation in multi-agent systems (MASs) is crucial for ensuring efficient collaboration among agents with different capabilities and skills. Traditional delegation models rely on social mechanisms such as trust and reputation to evaluate potential partners. While these approaches are effective in selecting competent agents, they often lack transparency, making it difficult for users to understand and trust the decision-making process. To address this limitation, we propose a novel task delegation model that integrates explainability through argumentation-based reasoning. Our approach employs the quantitative argumentation with votes framework (QuAD-V), a voting-based argumentation system that enables agents to justify their partner selection. We evaluate our model in a scenario involving the distribution of petroleum products via pipelines, where agents represent bases capable of temporarily storing a quantity of product. The connections between agents represent transportation routes, allowing the product to be sent from an origin to a destination base. The results demonstrate the effectiveness of our model in optimizing delegation decisions while maintaining clear, understandable explanations for agents’ decisions.