Year: 2025
Type: article
Abstract: With the rapid advancement of artificial intelligence technology, multi-agent systems are being widely applied in fields such as autonomous driving and robotic collaboration. However, existing methods often suffer from the disconnection between intention recognition and strategy optimization, leading to inefficiencies in group collaboration. This paper proposes a multi-agent cooperative group game model based on Intention-Strategy Optimization (ISO-MAGCG). The model establishes a two-layer optimization framework encompassing intention and strategy, enabling dynamic adaptation through the co-evolution of upper-layer intention recognition and lower-layer strategy optimization. A Group Attention-based Intention Recognition Network (GAIN) is designed to efficiently capture complex interactions among agents. Furthermore, an Adaptive Group Evolution Algorithm (AGEA) is proposed to ensure the stability of large-scale cooperative endeavors. Experiments conducted in navigation, resource collection, and defense collaboration scenarios validate the effectiveness of the proposed method. Compared with mainstream algorithms such as QMIX, MADDPG, and MAPPO, ISO-MAGCG demonstrates significant superiority in metrics including task success rate and cooperative efficiency, achieving an average improvement of 8.4% in task success rate, a 12% enhancement in cooperative efficiency, and an intention recognition accuracy of 94.3%. The results indicate notable performance advantages and favorable scalability.
Source: Algorithms
DOI: 10.3390/a19010022
Authors: Mingjun Tang, Renwen Chen, Junwu Zhu
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,930
OpenAlex ID: W7117150103