在权重选民模型中达成共识,使用无噪声和有噪声的观察结果
Ayalvadi Ganesh1, Sabine Hauert2, Emma Valla1
1School of Mathematics, University of Bristol, Bristol, BS8 1UG UK.
概括
群体机器人中的集体决策使用权重选民模型进行优化. 有限种群分析表明错误概率是有限的,即使是单一的最佳选择代理,确保了强大的群体共识.
科学领域:
- 群体机器人技术 群体机器人
- 集体决策 - 集体决策
- 统计物理 统计物理
背景情况:
- 集体决策对于群体机器人应用至关重要.
- 权重选民模型 (WVM) 解决了理论分析中最好的问题.
- 之前的WVM分析集中在热力学极限上,而不是有限的群体.
研究的目的:
- 为了对最好的两个模型进行精确的有限人群分析.
- 在完整和正规的网络拓上分析WVM.
- 调查测量误差对WVM中的剂量评估的影响.
主要方法:
- 为有限种群开发了一个精确的分析框架.
- 将分析应用于完整和正规的网络结构.
- 纳入了一种针对WVM中代理评估错误的新型分析.
主要成果:
- 在无需大量计算的情况下,在群体系统中预测两种最佳决策结果.
- 显示次优共识的错误概率是1的边界,不论人口大小.
- 证明随着最佳选项代理人数量的增加,错误概率接近零.
结论:
- 在有限集群系统中,WVM为集体决策提供了强大的机制.
- 有限人口分析为群体行为提供了准确的预测.
- 为一般的最好的n问题提供边界和近似值.
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