Heterogeneous Multiagent Task Allocation Based on Graph-Based Convolutional Assignment Neural Network

作     者：Ma, Ziyuan Gong, Huajun Xiong, Jun Wang, Xinhua 

作者机构：Nanjing Univ Aeronaut & Astronaut Coll Automat Engn Flight Control Lab Nanjing 210016 Peoples R China Nanjing Univ Posts & Telecommun Sch Internet Things Nanjing 210023 Peoples R China 

出 版 物：《IEEE INTERNET OF THINGS JOURNAL》 (IEEE Internet Things J.)

年 卷 期：2025年第12卷第11期

页      面：17281-17299页

核心收录：

学科分类：0810[工学-信息与通信工程] 0808[工学-电气工程] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Resource management Multi-agent systems Attention mechanisms Scalability Decision making Adaptation models Heuristic algorithms Dynamic scheduling Optimization Robustness Attention mechanism graph neural network (GNN) multiagent task allocation autonomous aerial vehicles (AAVs) unmanned ground vehicles (UGVs) unmanned surface vehicles (USVs) 

摘      要：Task allocation in complex multiagent systems involves assigning tasks to agents with varying capabilities to optimize overall performance. The challenge lies in selecting the most suitable agent for each task, considering the agents heterogeneity and the intricate relationships between tasks. Traditional methods often fail to capture this complexity. To address these limitations, we propose the graph multiagent task allocation neural network (GMATANN), a novel approach utilizing a graph attention mechanism. GMATANN models the interactions between agents and tasks through a task-agent graph, where both agents and tasks are represented as nodes, and their associations are depicted as edges. The graph attention mechanism is crucial for capturing the key relationships and ensuring effective information flow between nodes. By learning attention weights, the network automatically identifies which agents are best suited for specific tasks. We employ a neural network framework based on this attention mechanism to train and evaluate the method. Simulation experiments demonstrate the effectiveness of GMATANN, achieving a task allocation accuracy of 92.3% and a reliability of 94.2%, outperforming traditional approaches. This innovative method offers a new strategy for complex task allocation in multiagent systems, providing an adaptive solution that selects suitable agents for diverse tasks, thereby enhancing system efficiency.