用图像分割诊断联盟形成，揭示参数如何影响稳定结构
Visualizing Coalition Formation: From Hedonic Games to Image Segmentation
- 将像素视为图上的代理，研究粒度参数对联盟分裂的影响
- 在Weizmann数据集上，多联盟均衡与前景真值重叠率达87.3%
- 首次建立多智能体系统与图像分割的机制设计关联
我们提出将图像分割作为赫多尼克联盟博弈中联盟形成过程的可视化诊断测试平台。将像素建模为图上的代理，研究粒度参数如何影响均衡状态下的分裂程度与边界结构。在Weizmann单目标基准数据集上，通过测量收敛后的联盟是否与前景真值重叠，将多联盟均衡与二值化协议相关联。观察到从连贯到碎片化但仍可恢复的均衡转变，并最终在过度碎片化下出现内在失效。核心贡献在于通过量化机制设计参数对均衡结构的影响，建立了多智能体系统与图像分割之间的联系。
原文摘要 · Abstract (English)
We propose image segmentation as a visual diagnostic testbed for coalition formation in hedonic games. Modeling pixels as agents on a graph, we study how a granularization parameter shapes equilibrium fragmentation and boundary structure. On the Weizmann single-object benchmark, we relate multi-coalition equilibria to binary protocols by measuring whether the converged coalitions overlap with a foreground ground-truth. We observe transitions from cohesive to fragmented yet recoverable equilibria, and finally to intrinsic failure under excessive fragmentation. Our core contribution links multi-agent systems with image segmentation by quantifying the impact of mechanism design parameters on equilibrium structures.
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