## 2 Related Work
### Dynamic Changes in Agents' Behaviors  
In dynamic environments, agents' behavior can change rapidly, making it essential for consumer agents to detect these changes when using trust and reputation models to select reliable provider agents. Behavioral changes may stem from malicious intent, but they can also result from resource limitations, such as reduced battery power [2]. In MASs, dynamic behavior can be influenced by various factors, including seasonal variations, device malfunctions, malicious actions, network congestion, and more [4]. Some changes occur randomly and unpredictably, while others follow cyclical patterns. Additionally, group dynamics can contribute to behavioral changes, as relationships between agents evolve due to changing motivations or external influences, such as environmental changes. An example of agents exhibiting continuously changing behavior is found in Fifth-Generation (5G) networks, where forwarding entities are responsible for relaying packets along a route. These entities may drop packets, leading to a reduced packet delivery rate. Their behavior is inherently dynamic, as they can switch between malicious and legitimate states at any time [13]. Reinforcement Learning (RL) allows legitimate forwarding entities to learn about the behavior of other potential forwarders. However, malicious entities can also exploit RL to strategically drop packets while avoiding detection, thereby manipulating the learning environment of legitimate entities. To address this issue, [PERSON] et al. propose in [13] a hybrid trust model for secure routing in 5G networks. In this model, Q-learning enables distributed legitimate entities to evaluate the trustworthiness of neighboring forwarding entities. Additionally, using network-wide trust data and RL, a central authority can decide whether legitimate entities should continue or halt their learning process, depending on the proportion of legitimate to malicious entities in the network. However, the effectiveness of this model has not been experimentally validated. To handle dynamic behavior, current trust and reputation models continue to utilize methods like sliding windows and forgetting factors [4]. A sliding window of size \(n\) preserves only the \(n\) most recent experiences, based on the assumption that these interactions best reflect an agent's current behavior, while older records are discarded as they become less relevant. A forgetting factor gradually decreases the influence of past interactions over time, retaining all instances but assigning greater weight to more recent experiences. The trust model for pervasive computing applications proposed by [PERSON] et al. in [14] is an instance of the forgetting factor approach, as it progressively reduces the influence of past interactions through referral age in social trust calculations. It integrates both personal experience and reputation using fuzzy logic (FL) principles. The model consists of three subsystems, with two of the subsystems responsible for evaluating an entity's trust level based on individual experiences and referrals. To address the dynamic nature of trust, the calculation of social trust depends on two key factors: the referral age and the referrer reputation. Each referral is timestamped, and its influence on social trust decreases over time. Once it surpasses a predefined expiration point, it is no longer considered. The credibility of the referrer determines how much weight their referral carries in the final trust calculation. The system employs a central authority to manage storage and access to referrals. However, this introduces a single point of failure, which can be a limitation. After determining social and individual trust, each intelligent agent uses a weighted sum method to compute the final trust value. The third FL subsystem is responsible for determining the weight assigned to social trust, using as inputs the total number of social referrals and the total number of past individual interactions.