用《异星工厂》训练AI系统工程能力，解决动态复杂系统挑战
Develop AI Agents for System Engineering in Factorio
- 以《异星工厂》为沙盒环境，评估AI在动态系统中的综合决策能力
- 强调长期规划与不确定性应对，突破静态基准局限
- 适合关注智能系统设计、自主运维的科研与工业团队
前沿模型进展正推动AI代理的广泛应用。与此同时，全球对软件、制造、能源和物流等领域大型复杂系统的建设兴趣空前高涨。尽管基于AI的系统工程前景广阔，但当前主导的静态评测基准无法捕捉实现动态系统所需的关键能力，如权衡不确定性与主动适应性。本文主张通过面向自动化的沙盒游戏——特别是《异星工厂》——来训练和评估AI代理的系统工程能力。此举可使AI具备设计、维护和优化未来高难度工程项目所需的专门化推理与长程规划能力。
原文摘要 · Abstract (English)
Continuing advances in frontier model research are paving the way for widespread deployment of AI agents. Meanwhile, global interest in building large, complex systems in software, manufacturing, energy and logistics has never been greater. Although AI driven system engineering holds tremendous promise, the static benchmarks dominating agent evaluations today fail to capture the crucial skills required for implementing dynamic systems, such as managing uncertain trade-offs and ensuring proactive adaptability. This position paper advocates for training and evaluating AI agents' system engineering abilities through automation-oriented sandbox games-particularly Factorio. By directing research efforts in this direction, we can equip AI agents with the specialized reasoning and long-horizon planning necessary to design, maintain, and optimize tomorrow's most demanding engineering projects.
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