Power indices are essential in assessing the contribution and influence of individual agents in multi-agent systems, providing crucial insights into collaborative dynamics and decision-making processes. While invaluable, traditional computational methods for exact or estimated power indices values require significant time and computational constraints, especially for large $(n\ge10)$ coalitions. These constraints have historically limited researchers' ability to analyse complex multi-agent interactions comprehensively. To address this limitation, we introduce a novel Neural Networks-based approach that efficiently estimates power indices for voting games, demonstrating comparable and often superiour performance to existing tools in terms of both speed and accuracy. This method not only addresses existing computational bottlenecks, but also enables rapid analysis of large coalitions, opening new avenues for multi-agent system research by overcoming previous computational limitations and providing researchers with a more accessible, scalable analytical tool.This increased efficiency will allow for the analysis of more complex and realistic multi-agent scenarios.

本研究解决了传统计算方法在多智能体系统中计算权力指数时存在的时间和计算约束，尤其是在处理大规模联盟时的局限性。通过引入一种基于神经网络的新方法，该方法有效地估算投票游戏的权力指数，并在速度和准确性上表现出与现有工具相当或更优的性能，显著提高了对复杂多智能体交互的分析能力。

InfluenceNet：用于班兹哈夫和夏普莱值预测的人工智能模型