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[2602.13291] Agent Mars: Multi-Agent Simulation for Multi-Planetary Life Exploration and Settlement
Summary
Agent Mars presents a multi-agent simulation framework designed for efficient coordination in Mars base operations, addressing challenges of space exploration.
Why It Matters
As space exploration advances, effective coordination among humans and AI systems becomes crucial for mission success. Agent Mars offers a structured approach to manage complex interactions in a resource-scarce environment, paving the way for future multi-planetary settlements.
Key Takeaways
- Agent Mars simulates Mars base operations with 93 agents across seven command layers.
- It incorporates hierarchical coordination while allowing vetted cross-layer exchanges.
- The framework supports dynamic role handover and phase-dependent leadership.
- Agent Mars Performance Index (AMPI) quantifies team behavior and coordination trade-offs.
- The simulation identifies regimes for effective collaboration without compromising reliability.
Computer Science > Multiagent Systems arXiv:2602.13291 (cs) [Submitted on 9 Feb 2026] Title:Agent Mars: Multi-Agent Simulation for Multi-Planetary Life Exploration and Settlement Authors:Ziyang Wang View a PDF of the paper titled Agent Mars: Multi-Agent Simulation for Multi-Planetary Life Exploration and Settlement, by Ziyang Wang View PDF HTML (experimental) Abstract: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 oper...
