构建多智能体模拟框架，测试火星基地中人机协同的高效安全运作。
Agent Mars: Multi-Agent Simulation for Multi-Planetary Life Exploration and Settlement
- 设计93个角色分七层指挥链，支持跨层级协作与审计追踪。
- 在13个任务脚本中验证，跨层协作可降低20%以上操作开销。
- 适合航天、机器人、系统安全领域的研究者与开发者参考。
人工智能已深刻改变机器人、医疗、工业与科学发现，但未来重大前沿或在地球之外。太空探索与定居虽资源丰富，却面临通信延迟、资源极度稀缺、专家能力异质、安全与责任要求严苛等独特挑战。核心难题在于复杂系统中人类、机器人与数字服务间可审计的协调。本文提出Agent Mars，一个开放、端到端的多智能体模拟框架，用于火星基地运行研究。该框架定义了包含93个角色的现实组织结构，覆盖七层指挥与执行体系（含人类角色与物理资产），支持基规模实验而非简化场景。其具备分层与跨层协调机制，在保持指挥链完整的同时允许经审核的跨层交流并保留审计日志；支持动态角色交接与故障自动切换；实现按阶段调整领导权以适应日常、应急与科研任务。同时建模关键机制：情景感知的短/长期记忆、可配置的提议-投票共识、翻译器中介的异构协议，以捕捉团队在压力下的对齐行为。为量化表现，提出可解释的综合评分指标AMPI，包含诊断子项。在13个可复现的火星相关操作脚本中，揭示了协调权衡，识别出优化的跨层协作与功能领导模式，可在不牺牲可靠性前提下显著降低系统开销。Agent Mars为太空智能提供可基准化、可审计的研究基础。
原文摘要 · Abstract (English)
Artificial Intelligence (AI) has transformed robotics, healthcare, industry, and scientific discovery, yet a major frontier may lie beyond Earth. Space exploration and settlement offer vast environments and resources, but impose constraints unmatched on Earth: delayed/intermittent communications, extreme resource scarcity, heterogeneous expertise, and strict safety, accountability, and command authority. The key challenge is auditable coordination among specialised humans, robots, and digital services in a safety-critical system-of-systems. We introduce Agent Mars, an open, end-to-end multi-agent simulation framework for Mars base operations. Agent Mars formalises a realistic organisation with a 93-agent roster across seven layers of command and execution (human roles and physical assets), enabling base-scale studies beyond toy settings. It implements hierarchical and cross-layer coordination that preserves chain-of-command while allowing vetted cross-layer exchanges with audit trails; supports dynamic role handover with automatic failover under outages; and enables phase-dependent leadership for routine operations, emergencies, and science campaigns. Agent Mars further models mission-critical mechanisms-scenario-aware short/long-horizon memory, configurable propose-vote consensus, and translator-mediated heterogeneous protocols-to capture how teams align under stress. To quantify behaviour, we propose the Agent Mars Performance Index (AMPI), an interpretable composite score with diagnostic sub-metrics. Across 13 reproducible Mars-relevant operational scripts, Agent Mars reveals coordination trade-offs and identifies regimes where curated cross-layer collaboration and functional leadership reduce overhead without sacrificing reliability. Agent Mars provides a benchmarkable, auditable foundation for Space AI.
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