  Large Language Models (LLMs) have the capacity of performing complex
scheduling in a multi-agent system and can coordinate these agents into
completing sophisticated tasks that require extensive collaboration. However,
despite the introduction of numerous gaming frameworks, the community has
insufficient benchmarks towards building general multi-agents collaboration
infrastructure that encompass both LLM and human-NPCs collaborations. In this
work, we propose a novel infrastructure - MindAgent - to evaluate planning and
coordination emergent capabilities for gaming interaction. In particular, our
infrastructure leverages existing gaming framework, to i) require understanding
of the coordinator for a multi-agent system, ii) collaborate with human players
via un-finetuned proper instructions, and iii) establish an in-context learning
on few-shot prompt with feedback. Furthermore, we introduce CUISINEWORLD, a new
gaming scenario and related benchmark that dispatch a multi-agent collaboration
efficiency and supervise multiple agents playing the game simultaneously. We
conduct comprehensive evaluations with new auto-metric CoS for calculating the
collaboration efficiency. Finally, our infrastructure can be deployed into
real-world gaming scenarios in a customized VR version of CUISINEWORLD and
adapted in existing broader Minecraft gaming domain. We hope our findings on
LLMs and the new infrastructure for general-purpose scheduling and coordination
can help shed light on how such skills can be obtained by learning from large
language corpora.
