Coordinating robotic teams across multiple concurrent search tasks is a critical challenge in search and rescue operations. This work presents a new multi-agent framework designed to manage and optimize search efforts when several missing-person reports occur in parallel. The method extends iso-probability curve-based trajectory planning to the multi-target case and introduces a dynamic task allocation scheme that distributes search agents (e.g., UAVs) across tasks according to evolving probabilities of success. Overlapping search regions are explicitly resolved to eliminate duplicate coverage and to ensure balanced effort among tasks. The framework also extends the behavior-based motion prediction model for missing persons and the non-parametric estimator for iso-probability curves to capture more realistic search conditions. Extensive simulated experiments, with multiple concurrent tasks, demonstrate that the proposed method tangibly improves mean detection times compared with equal-allocation and individual static assignment strategies.
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