GCS-Q:量子图联盟结构生成

Supreeth Mysore Venkatesh, A. Macaluso, M. Klusch
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引用次数: 4

摘要

对于给定的理性主体联盟博弈,生成最优联盟结构的问题是找到一个使其社会福利最大化且已知为np困难的分区。针对联盟结构生成中的诱导子图博弈(isg),提出了一种新的量子支持解GCS-Q。GCS-Q首先将大联盟作为初始联盟结构,然后将联盟迭代划分为两个非空子集,得到一个联盟值更高的联盟结构。特别是,给定一个$n$-agent ISG, GCS-Q使用量子退火设备解决$\mathcal{O}(n)$次的最优分割问题,在每一步探索$\mathcal{O}(2^n)$分区。我们表明,GCS-Q在标准基准数据集上的运行时间为$n^2$,预期最坏情况近似比为$93\%$,优于目前最好的经典求解器。
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GCS-Q: Quantum Graph Coalition Structure Generation
The problem of generating an optimal coalition structure for a given coalition game of rational agents is to find a partition that maximizes their social welfare and is known to be NP-hard. This paper proposes GCS-Q, a novel quantum-supported solution for Induced Subgraph Games (ISGs) in coalition structure generation. GCS-Q starts by considering the grand coalition as initial coalition structure and proceeds by iteratively splitting the coalitions into two nonempty subsets to obtain a coalition structure with a higher coalition value. In particular, given an $n$-agent ISG, the GCS-Q solves the optimal split problem $\mathcal{O} (n)$ times using a quantum annealing device, exploring $\mathcal{O}(2^n)$ partitions at each step. We show that GCS-Q outperforms the currently best classical solvers with its runtime in the order of $n^2$ and an expected worst-case approximation ratio of $93\%$ on standard benchmark datasets.
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