HSD-PA: Hierarchical Semantic Decomposition and Predictive Adaptive Scheduling for Distributed Intelligent Agent Systems

Abstract

Distributed intelligent agent systems have become a fundamental paradigm for large-scale autonomous decision-making in dynamic environments characterized by fluctuating workloads, time-varying communication delays, heterogeneous computational resources, and evolving task dependencies and resource-constrained environments. However, existing approaches to task decomposition and scheduling often rely on static structures or reactive strategies, which fail to capture evolving task semantics and lack predictive adaptability. To address these limitations, we propose HSD-PA, a hierarchical semantic decomposition and predictive adaptive scheduling framework for distributed task execution. HSD-PA introduces a semantic-aware task graph to model dynamically evolving task dependencies, enabling flexible and context-aware decomposition. A hierarchical mechanism further refines sub-task representations based on real-time system states. In addition, a predictive scheduling strategy estimates agent workload, communication latency, and execution reliability to support look-ahead decision-making, thereby mitigating bottlenecks and improving coordination efficiency. A distributed consistency mechanism is also incorporated to achieve scalable coordination with partial local information. Experimental results on multiple datasets demonstrate that HSD-PA consistently improves task completion time, system throughput, and robustness compared with state-of-the-art methods, particularly under workload-dynamic, communication-uncertain, and heterogeneous multi-agent environments.