用神经网络快速预测投票博弈中的影响力，比传统方法又快又准。
InfluenceNet: AI Models for Banzhaf and Shapley Value Prediction
- 基于神经网络构建预测模型，替代传统计算方式
- 在n≥10的大联盟中速度与精度均优于现有方法
- 适合需要分析复杂多智能体系统的研究人员
权力指数在评估多智能体系统中个体贡献和影响力方面至关重要，为协作动态与决策过程提供关键洞察。尽管价值巨大，传统计算方法在精确或估算权力指数时需耗费大量时间和计算资源，尤其在大规模（n≥10）联盟中更为显著，长期限制了对复杂多智能体交互的全面分析。为此，我们提出一种基于神经网络的新方法，可高效估计投票博弈中的权力指数，在速度与准确性上表现相当甚至超越现有工具。该方法不仅突破了原有计算瓶颈，还实现了对大联盟的快速分析，为多智能体系统研究开辟新路径，使研究人员能更便捷、可扩展地开展复杂真实场景的分析。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。