AI agent在Minecraft为 " 建设繁荣的文明 " 而发动的群战——50-agent团队在陷入瘫痪的官僚机构之后失败
希望游戏测试能够提供对最近AI agent头条的洞察，好坏。
酵母和奥拉姆实验室的AI研究人员最近在游戏场景中测试了AI agent群。 Minecraft 及其各种游戏模式是沙盒，其中释放了 100 个agents 以合作或作为对手。 这个想法似乎是，游戏中的合作和冲突将使人们深入了解涉及AI agents的真实世界头条抢案。
这项研究可能有助于我们了解最近AI 集群案件中发生的好事和坏事，因此，这一技术的开发者可以加以改进。 博客提到两起形成对比的案例，即“最近消除数百个开放的数学问题的千agent群案”和“极其危险的”OpenAI-Hugging Face事件。 这项研究以以前的工作和框架为基础，具体来说，是像Mindcraft和Mineflayer的脚本工具那样重置。
Minecraft的多agent动态
第一个AI 集群测试计划是一个简单的协作计划。 在这一测试阶段，研究人员责成50agents在3小时内建设繁荣的文明。 它似乎在这里测试了50GPT-6.1 Sol agents，但没有任何Claude agents.
可能从这个“Civ50”测试中最有趣的见解是，50人的殖民地从未真正繁荣。 在第一个晚上看到僵尸拿出大量agents(但惩罚很少)之后，人们注意到“许多雄心勃勃的初始任务被放弃了”。
此后，殖民者小心翼翼地存活下来，出现了官僚主义倾向。 例如，“四个agents花了11分钟任命一个`单一化肥操作员 '，在单一小麦块上做一块骨头饭。” 研究者说，这种AI agent 集群的行为“感觉类似于在与编码agents合作时看到的规律。”
接下来，研究者们决定将AI agent群落陷入冲突。 他们首先在Bed Wars测试它们，这是一种PvP攻占基地的团队，同时保卫你的游戏模式。 设置为10GPT‐6.1 Sol对10Claude Opus 5.5agents.
研究人员指出，“GPT agents提出了更加积极和详细的战略”。 Claude更具有防御性。 还值得注意的是，各小组在处理问题时存在差异。 “一个工具错误停止了拆卸。 GPT诊断出来，并分享了工作机会；若干Claudes结论断裂是不可能的。”
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最后，agents在Mindustry进行了测试，是Minecraft中的一种具有塔防模式的轻型RTS战斗游戏。 "GPTs在7场比赛中以6-1战胜Claudes,"这对OpenAI创造的agents来说是一个伟大的结果。 研究者得出结论，GPTs的行为更像是蜂巢，而Claudes则有一个单一的领袖，但有民主式的决策过程。 在M工业，前者是更一贯的赢取战略的基础。
这些蜂群是开放源代码的，这样你就可以自己运行，如果你有兴趣调整和测试的话。 提供一些调查调查方向。 例如，有人建议，比较Minecraft中不同模型组合和不同大小的agent群可能会得出值得的结论。
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AI agent swarms unleashed in Minecraft to ‘build a thriving civilization’ — 50-agent teams fail after falling into paralyzing bureaucracy
It is hoped game testing can provide insight into recent AI agent headlines, good and bad.
AI researchers at ycombinator and Olam Labs recently tested AI agent swarms in gaming scenarios. Minecraft and its various game modes were the sandboxes where 100s of agents were unleashed to work together or as adversaries. The idea seems to be that the in-game collaborations and conflicts would provide insight into real-world headline-grabbing cases with AI agents involved.
This study might help us see what happened, good and bad, in recent AI swarm cases, and thus developers of this technology can improve it. The blog mentions two contrasting cases, which were the thousand-agent swarms that “recently obliterated hundreds of open mathematical problems,” and the “extremely dangerous” OpenAI-Hugging Face incident. This research is built on prior work and frameworks, specifically repos like Mindcraft and scripting tools in Mineflayer.
Multi-agent dynamics in Minecraft
The first AI swarm testing plan was a simple collaborative one. During this test phase, the researchers tasked 50 agents with building a thriving civilization in three hours. It seems to have tested 50 GPT-6.1 Sol agents here, but not any Claude agents.
Probably the most interesting insight from this ‘Civ50’ test was that the 50-strong colony never really thrived. After the first night saw zombies take out a large number of agents (but with little penalty) it was noted that “many ambitious initial tasks were abandoned.”
From then on, the colonists cautiously survived, and there was a tendency toward bureaucracy. As an example, “four agents spent 11 minutes appointing a ‘sole fertilizer operator’ for a single piece of bone meal on a single wheat block.” The researchers say this AI agent swarm behavior “felt similar to patterns seen while working with coding agents.”
Next up, the researchers decided to put AI agent swarms into conflict. First they tested them in Bed Wars, which is a kind of team PvP capture the base, while defending yours game mode. The setup was 10 GPT‑6.1 Sol vs. 10 Claude Opus 5.5 agents.
“GPT agents came up with much more aggressive and elaborate strategies,” noted the researchers. Claude was more defensive. It was also interesting to observe contrasts in how the teams handled problems. “A tool bug stopped block-breaking. GPT diagnosed it and shared workarounds; several Claudes concluded breaking was impossible.”
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Finally, the agents were tested in Mindustry, a kind of light RTS combat game with tower defense mode in Minecraft. “The GPTs won 6-1 against the Claudes in the 7 games,” which was a great result for the OpenAI-created agents. Researchers concluded that GPTs acted more like a hive, while Claudes had a single leader but a democratic-style decision process. In Mindustry, the former was the foundation for a more consistently winning strategy.
These swarms are open sourced so you can run your own, if you are interested in tweaking and testing. The source blog post ends with some open questions that you can check hands-on, offering some directions for investigative probing. For example, it is suggested that comparing different mixes of models and varying sizes of agent swarms in Minecraft might raise worthwhile conclusions.
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Mark Tyson is a news editor at Tom's Hardware. He enjoys covering the full breadth of PC tech; from business and semiconductor design to products approaching the edge of reason.
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