# Transparent Task Delegation in Multi-Agent Systems Using the QuAD-V Framework
## 1 Introduction  
Multi-agent systems (MASs) play a fundamental role in various domains, enabling distributed problem-solving and autonomous collaboration among agents [1, 2, 3]. In many applications, agents must delegate tasks to others, taking advantage of different capabilities and expertise to achieve common goals [4, 5, 6]. Task delegation mechanisms typically rely on social evaluation metrics, such as trust and reputation, to assess potential partners [7, 8, 9, 10, 11, 12]. In such cases, agents learn about their partners' behaviors through direct and indirect experiences, as well as observations of environmental conditions [13, 14]. For example, in [9], a Fuzzy Cognitive Map (FCM)-based structure [15] is used to integrate different trust components into a performance measure that agents use to infer the probability of delegation associated with a partner. Similarly, a reputation-based delegation model is proposed in [10], where the partners are selected based on a performance metric computed from direct social evaluations or third-party assessments. Finally, in [12], partners are chosen based on a performance measure represented as a reward factor that is updated as tasks are delegated among agents, considering direct experiences between delegators and their partners. However, in general, such models produce only a quantitative measure that influences partner selection, without providing an explicit explanation of whya particular partner is preferred. This lack of explainability can undermine trust in autonomous decision-making systems, particularly in critical domains where understanding the rationale behind decisions is essential, such as medicine or finance [16; 17; 18]. Explainability in computational systems has gained significant attention in recent years, as users increasingly demand transparency in automated processes [19; 20]. The need for transparency arises from the growing integration of _artificial intelligence_ (AI) and autonomous systems, particularly in domains where automated decision-making affects human lives [21; 22]. As these systems become more complex, users not only expect effective solutions but also a clear understanding of the reasoning behind them. Explainability is particularly valuable in high-stakes domains, such as autonomous vehicles, military operations, and critical infrastructure management, where the consequences of poor decisions can be severe [23; 24; 25]. A solution that has been explored to generate explanations for computational systems is _formal argumentation_ (FA). FA enables the generation of explanations by analyzing a set of arguments and the relationships established between them [26]. In the literature, different types of FA are presented, such as the _abstract argumentation framework_ (AAF) introduced in [27], where arguments are treated as atomic entities without internal structure. This framework is formally defined as a set of arguments, and a conflict relation between them can be represented as a directed graph. An AAF does not require complex rules or advanced logical processing to resolve conflicts but has limited expressiveness, as it does not account for support relations between arguments. On the other hand, more sophisticated approaches, such as _structured argumentation frameworks_ (i.e., ABA [28], ASPIC+ [29], and DeLP [30]), build argumentation based on logical rules and deductions, allowing for the formal modeling of arguments within a robust formalism. However, these approaches require specific grammars and generally present greater complexity for integration into applications without specialized libraries. In this work, we propose a novel task delegation model that incorporates explainability through argumentation-based reasoning. Unlike traditional delegation models that rely solely on trust or reputation scores to select partners, our approach not only computes an evaluation metric but also justifies the decision by explaining the underlying reasons for a partner's acceptance or rejection.