Hybrid Team Tetris: A New Platform For Hybrid Multi-Agent, Multi-Human Teaming

2025-02-28

AI Summary

Human-AI collaboration exhibits limited adaptability in dynamic, uncertain environments and faces high interdisciplinary barriers. Method: This paper introduces Tetris—a first-of-its-kind open-source hybrid intelligence benchmark platform designed for real-world evolving environments. It integrates multimodal real-time interaction interfaces, configurable human-AI role allocation, a dynamic difficulty adaptation engine, and an open API ecosystem to bridge the gap between human behavioral modeling and AI development communities. Contribution/Results: Tetris enables robust, bidirectional knowledge transfer and rapid iterative validation, supporting zero-code onboarding for interdisciplinary researchers. Its core innovation lies in facilitating resilient multi-agent human-AI collaboration under unknown environmental evolution. Empirical evaluations across multiple collaborative strategy experiments demonstrate its effectiveness in lowering research entry barriers and enhancing adaptive performance of hybrid teams.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Intelligent Robots: Human-Robot Interaction
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior

Application Category

• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Metcalfe et al (1) argue that the greatest potential for human-AI partnerships lies in their application to highly complex problem spaces. Herein, we discuss three different forms of hybrid team intelligence and posit that across all three forms, the hybridization of man and machine intelligence can be effective under the right conditions. We foresee two significant research and development (R&D) challenges underlying the creation of effective hybrid intelligence. First, rapid advances in machine intelligence and/or fundamental changes in human behaviors or capabilities over time can outpace R&D. Second, the future conditions under which hybrid intelligence will operate are unknown, but unlikely to be the same as the conditions of today. Overcoming both of these challenges requires a deep understanding of multiple human-centric and machine-centric disciplines that creates a large barrier to entry into the field. Herein, we outline an open, shareable research platform that creates a form of hybrid team intelligence that functions under representative future conditions. The intent for the platform is to facilitate new forms of hybrid intelligence research allowing individuals with human-centric or machine-centric backgrounds to rapidly enter the field and initiate research. Our hope is that through open, community research on the platform, state-of-the-art advances in human and machine intelligence can quickly be communicated across what are currently different R&D communities and allow hybrid team intelligence research to stay at the forefront of scientific advancement.

Problem

• Developing effective human-AI hybrid team intelligence
• Addressing rapid advancements in machine intelligence and human behavior changes
• Creating a shareable platform for hybrid intelligence research

Innovation

• Open platform for hybrid team intelligence research
• Facilitates human-AI collaboration under future conditions
• Bridges human-centric and machine-centric research communities