随机选择可促进人工智能体求解集体网络着色问题
Random Choices can Facilitate the Solving of Collective Network Coloring Problems by Artificial Agents
AI总结：
本文研究局部信息下短视人工智能体的网络着色问题，证明在决策推理中精细引入随机选择可帮助分布式贪心优化逃离局部极小并实现全局协调。
摘要：
要解决从网络着色到公地悲剧等各种具有挑战性的集体行动问题，需要全局协调。近期的实证研究表明，少数带噪声的自主智能体的存在可以大幅提升人类在求解网络化颜色协调博弈时的集体表现。为了进一步从分析层面理解行为随机性的作用，我们在此研究短视的人工智能体尝试求解类似的网络着色问题；这些智能体使用仅基于局部信息的决策更新规则，但在其启发式推理的不同阶段允许随机选择。我们考虑智能体分布在一个随机二分网络上，该网络保证可以用两种颜色求解。通过基于智能体的模拟和理论分析，我们表明，由此产生的颜色冲突解决效能取决于智能体随机行为的具体实现方式，包括带噪声智能体的比例以及噪声是在哪个决策阶段引入的。此外，可以针对特定的底层群体结构（例如网络规模和平均网络度）精细调节行为随机性，以便在寻找集体着色方案时产生有利结果。我们的工作表明，利用局部信息的分布式贪心优化算法应当与通过随机选择进行的偶尔探索相结合，以克服局部极小值并实现全局协调。
英文摘要：
Global coordination is required to solve a wide variety of challenging collective action problems from network colorings to the tragedy of the commons. Recent empirical study shows that the presence of a few noisy autonomous agents can greatly improve collective performance of humans in solving networked color coordination games. To provide further analytical insights into the role of behavioral randomness, here we study myopic artificial agents attempt to solve similar network coloring problems using decision update rules that are only based on local information but allow random choices at various stages of their heuristic reasonings. We consider that agents are distributed over a random bipartite network which is guaranteed to be solvable with two colors. Using agent-based simulations and theoretical analysis, we show that the resulting efficacy of resolving color conflicts is dependent on the specific implementation of random behavior of agents, including the fraction of noisy agents and at which decision stage noise is introduced. Moreover, behavioral randomness can be finely tuned to the specific underlying population structure such as network size and average network degree in order to produce advantageous results in finding collective coloring solutions. Our work demonstrates that distributed greedy optimization algorithms exploiting local information should be deployed in combination with occasional exploration via random choices in order to overcome local minima and achieve global coordination.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
