The increasing integration of artificial intelligence (AI) in everyday life brings with it new challenges and questions for regarding how humans interact with autonomous agents. Multi-agent experiments, where humans and AI act together, can offer important opportunities to study social decision making, but there is a lack of accessible tooling available to researchers to run such experiments. We introduce two tools designed to reduce these barriers. The first, CoGrid, is a multi-agent grid-based simulation library with dual NumPy and JAX backends. The second, Multi-User Gymnasium (MUG), translates such simulation environments directly into interactive web-based experiments. MUG supports interactions with arbitrary numbers of humans and AI, utilizing either server-authoritative or peer-to-peer networking with rollback netcode to account for latency. Together, these tools can enable researchers to deploy studies of human-AI interaction, facilitating inquiry into core questions of psychology, cognition, and decision making and their relationship to human-AI interaction. Both tools are open source and available to the broader research community. Documentation and source code is available at {cogrid, multi-user-gymnasium}.readthedocs.io. This paper details the functionality of these tools and presents several case studies to illustrate their utility in human-AI multi-agent experimentation.

翻译：人工智能在日常生活中的日益融合，为人类如何与自主智能体互动带来了新的挑战与问题。人类与人工智能共同参与的多智能体实验，为研究社会决策提供了重要机遇，但当前研究者缺乏易于使用的工具来开展此类实验。我们引入两款旨在降低这些门槛的工具。其一是CoGrid，一个基于网格的多智能体仿真库，支持NumPy和JAX双后端。其二是多用户实验场（Multi-User Gymnasium，MUG），该工具可直接将此类仿真环境转化为交互式网络实验。MUG支持任意数量人类与人工智能实体的交互，采用服务器权威或点对点网络架构，并配备回滚网络代码以应对延迟问题。这两款工具协同作用，可助力研究者部署人机交互研究，促进对心理学、认知与决策等核心问题及其与人机交互关系的探究。两款工具均已开源，面向广大研究社区开放使用。文档与源代码可于{cogrid, multi-user-gymnasium}.readthedocs.io获取。本文详细阐述了这些工具的功能，并通过若干案例研究展示了其在人机多智能体实验中的实用价值。