Argonaut：面向分布式优化的交互式可视化探索工具
Argonaut: Interactive Visual Exploration for Distributed Optimization
AI总结：
Argonaut是一款轻量级容器化优化仪表盘，可交互式可视化探索去中心化多智能体离散选择优化的全搜索过程，在三类真实数据集上验证了其高效性。
AI中文摘要：
在去中心化场景下的分布式离散选择优化问题，往往难以探索与导航：随着系统规模扩大，要理清其他智能体的选择、这些选择的相互依赖关系，以及它们如何共同达成全局目标，很快会变得棘手，核心限制在于搜索过程的可观测性。现有方法大多是集中式的，支持有限，仅可视化最终解或在固定数据集上提供算法后端，因此解的形成过程仍是黑箱。我们提出Argonaut，这是一个轻量级、容器化的优化仪表盘，支持对去中心化场景下多智能体离散选择优化的整个搜索过程进行交互式可视化探索。用户可上传数据集、构建智能体与选项、动态修改决策空间及其参数，运行多个算法后端以查看每种配置如何影响局部智能体决策及最终全局目标。Argonaut将系统构建、优化与分析统一为一个交互式循环，是同类工具中的首个，它让分布式离散选择优化成为人机协同过程，而非一次性黑箱计算。我们在真实世界的家庭用电、共享出行及传感器数据交换数据集上对Argonaut进行评估，该数据集可扩展至5600个智能体，暴力搜索下最多生成100万个解。它基于this http URL接口构建，具备可扩展的Java和Python优化后端，在100个决策属性下处理200个智能体时，典型运行时间不到30秒。
英文摘要：
Distributed discrete-choice optimization in decentralized settings is often hard to explore and navigate: disentangling what other agents choose, how their choices are interdependent, and how they collectively reach a global objective quickly becomes intractable as the system scales. The major limitation is observability of the search process. Existing methods are largely centralized and offer limited support, visualizing only the final solution or providing algorithm backends over a fixed dataset, so how a solution is reached stays a black box. We present Argonaut, a lightweight, containerized optimization dashboard that enables interactive, visual exploration of the entire search process for multi-agent discrete-choice optimization in decentralized settings. Users upload datasets, construct agents and options, modify the decision space and its parameters on the fly, and run multiple algorithm backends to inspect how each configuration shapes local agent decisions and the resulting global objective. By uniting system construction, optimization, and analysis in one interactive loop, the first of its kind, Argonaut makes distributed discrete-choice optimization a human-in-the-loop process rather than a one-shot, black-box computation. We evaluate Argonaut on real-world household-electricity, shared-mobility, and sensor-data-exchange datasets scaling to 5600 agents and up to 1M solutions under brute force. Built on a Node.js interface with extensible Java and Python optimization backends, it maintains a typical runtime of 200 agents over 100 decision attributes in under 30 seconds.
