Investigating Movement and Area Loyalty through Agent-Based Modelling Social insects are differentiated into functional groups (nurses, feeding, breeding groom, and foragers that bring food back to the nest), in which tasks are assigned to different areas of the nest. Additionally ,social insects are usually responsible for multiple tasks and this task profile can change over time. Investigating how insects find and remain in their specific work areas is crucial for understanding the efficiency of their division of labor. It has been suggested that the working movements of social insects are consistent with two local movement mechanisms: one in which workers increase movement diffusivity outside their primary zone (locomotor adjustment), and another in which workers modulate turning behaviour when approaching zone boundaries (boundary effect). Here, we study the patterns and mechanisms of movement associated with area loyalty in ants. Specifically, our research involves examining  the  searching mechanisms and locomotor adjustments in social insects as they navigate and remain within their designated work areas. To achieve this, we employed  training an agent-based model in a specific setting , in which the movement is decided by the prediction of an artificial neural network to identify key behavioural patterns and to simulate potential scenarios based on these patterns. The simulated world in which the agent is moving is an endless plane with specific predefined working areas. We collect and analyse empirical movement data and then compare its behaviour with it , focusing on how individuals navigate and remain within spatial zones. Our study provides new insights into the universal strategies for spatial fidelity in ants and contributes to the broader understanding of other searching behaviours in animals, such as  searching mechanisms involved in foraging.