## 2 Related Work
### Dynamic Changes in Agents' Behaviors
Assessing trust in MASs is particularly challenging due to the dynamic nature of agent behavior, which can change at varying speeds and times. This variability makes traditional approaches, such as forgetting old data at a constant rate and sliding windows, ineffective [4]. To address this issue, [PERSON] and [PERSON] in [4] propose RaPTaR, a framework that sits on top of existing trust and reputation models to detect and adapt to behavior changes within agent groups. RaPTaR enhances trust algorithms by supplying past experience data that have been statistically evaluated to reflect an agent's current behavior. The system identifies behavioral shifts by analyzing the outcome patterns of agent groups using the Kolmogorov-Smirnov (K-S) statistical test. Additionally, it records transitions between behavior changes to recognize recurring patterns, which can be leveraged for future predictions. By exploiting repetitive behavior, RaPTaR improves trust assessment in dynamic environments. The experimental results indicate that while RaPTaR effectively detects and responds to random behavioral changes, its accuracy remains limited and can be improved. Reputation-based trust models are widely used in distributed networks, but they encounter two major challenges: the transmitted reputation may not accurately reflect an entity's current trustworthiness, and over-reliance on reputation makes the system vulnerable to collusion, where malicious entities conspire to fabricate trust values. One possible solution is trust prediction, which estimates an entity's current trustworthiness based on past interactions, behavioral history, and other objective factors. Traditional time-based prediction methods fall into two categories. One is the complete arithmetic mean, which computes the average of all past data to predict the next trust value. The other method is mean shift, which prioritizes recent data, ignoring long-term history. A more refined approach is exponential smoothing, which balances both methods by incorporatingpast data while assigning diminishing weight as time elapses. However, pure exponential smoothing struggles with highly volatile data, especially in cases where a service provider is hijacked by adversaries, causing an abrupt shift from high trust to deception. In such situations, exponential smoothing alone can lead to significant deviations in trust estimation. To address this issue, [PERSON] et al. in [15] propose a dynamic trust model that integrates direct and indirect trust computation with trust prediction. Their approach first applies exponential smoothing to compute a trend curve and then utilizes a Markov chain model to adjust for fluctuations, improving prediction accuracy. This is a refined method for handling abrupt behavioral shifts, such as when an entity transitions from trustworthy to malicious. In the proposed CA algorithm, after delivering a service, a service provider reassesses its performance. If the performance falls below a predefined threshold, the service provider categorizes itself as a bad provider. This ensures that each provider continuously maintains an updated assessment of its capabilities, enabling the prompt detection of performance declines that could negatively impact consumers.