# Transparent Task Delegation in Multi-Agent Systems Using the QuAD-V Framework
## 6 Conclusions  
In this work, we presented a task delegation model built upon a voting-based argumentation framework (QuAD-V framework). Using this model, agents (delegators) can select their partners (delegatees) for a given task by considering three evaluation dimensions: effectiveness, availability, and competencies. These dimensions are translated into an acceptability measure (acceptance score), which, in turn, summarizes the partner's behavior as a delegate. However, it is important to note that our model is flexible and can accommodate additional characteristics by incorporating new argumentation criteria into its structure, allowing it to be adapted to different delegation scenarios. The main contribution of the proposed delegation model is its capability to generate explanations for the delegators' choices. Due to the adoption of the QuAD-V framework, our model can justify the delegators' deliberative actions by providing the reasons behind the selection of a certain partner. Furthermore, the modeling approach introduced in  
Figure 4: Local explanation of base \(b_{3}\)’s behavior as a delegate in iteration 25. Figure 5: Global explanation of the choice of the best route (\(b_{0}\) (_root_), \(b_{2}\), \(b_{3}\), \(b_{4}\), \(b_{6}\), \(b_{8}\), and \(b_{9}\) (_terminator_)) produced during iteration 100.  
this work can be easily adapted to different contexts. As discussed in our case study, the framework was also employed to generate explanations justifying why one transportation route is better than others. As future work, we intend to apply the proposed model to explain partner selection in dynamic environments where agents can enter and leave the system, as well as exhibit behavioral changes over time. Thus, our model could be used not only to justify the choice of a potential partner but also to explain the reasons behind behavioral changes. For instance, it would be possible to identify the reasons that lead a partner who previously demonstrated good behavior--such as meeting performance estimates and completing assigned tasks--to start failing in its estimations and in the execution of delegated tasks. Additionally, we aim to explore our model to generate counterfactual explanations. Counterfactuals are particularly useful for understanding how different decisions or conditions could have led to alternative outcomes. In the context of task delegation, counterfactual explanations can help answer questions such as: \"What if a different partner had been chosen?\" or \"What if a delegatee had a higher acceptance score?\" Such elements contribute to transparency and trust in decision-making processes and are particularly valuable in scenarios where human agents collaborate with artificial agents. To conclude, in this work, we assume that agents vote based on threshold values, which ensures interpretability and structured evaluation. However, in practical applications, voting motivations can be complex and context-dependent. To better capture this complexity, we propose three possible extensions to the current threshold-based approach: thresholds could be dynamically adjusted over time using machine learning techniques based on delegators' experiences in evaluating their partners; different delegators may have distinct evaluation criteria, so thresholds could be personalized based on user profiles; and replacing strict thresholds with an interval-valued membership function would allow for smoother transitions between positive and negative votes. These extensions would enable the model to better accommodate diverse voting behaviors while preserving interpretability and structured evaluation. Conceptualization, J.J.B., M.M.-E. and C.A.T. ; methodology, J.J.B., M.M.-E. and C.A.T. ; software, J.J.B. ; validation, J.J.B. ; formal analysis, J.J.B. and C.A.T. ; investigation, J.J.B., M.M.-E. and C.A.T. ; resources, J.J.B. ; data curation, J.J.B. ; writing--original draft preparation, J.J.B. ; writing--review and editing, J.J.B., M.M.-E. and C.A.T. ; visualization, J.J.B. ; supervision, C.A.T.