Ad-Hoc Human-AI Coordination Challenge
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866912455668531200 |
|---|---|
| author | Dizdarević, Tin Hammond, Ravi Gessler, Tobias Calinescu, Anisoara Cook, Jonathan Gallici, Matteo Lupu, Andrei Muglich, Darius Forkel, Johannes Foerster, Jakob Nicolaus |
| author_facet | Dizdarević, Tin Hammond, Ravi Gessler, Tobias Calinescu, Anisoara Cook, Jonathan Gallici, Matteo Lupu, Andrei Muglich, Darius Forkel, Johannes Foerster, Jakob Nicolaus |
| contents | Achieving seamless coordination between AI agents and humans is crucial for real-world applications, yet it remains a significant open challenge. Hanabi is a cooperative card game featuring imperfect information, constrained communication, theory of mind requirements, and coordinated action -- making it an ideal testbed for human-AI coordination. However, its use for human-AI interaction has been limited by the challenges of human evaluation. In this work, we introduce the Ad-Hoc Human-AI Coordination Challenge (AH2AC2) to overcome the constraints of costly and difficult-to-reproduce human evaluations. We develop \textit{human proxy agents} on a large-scale human dataset that serve as robust, cheap, and reproducible human-like evaluation partners in AH2AC2. To encourage the development of data-efficient methods, we open-source a dataset of 3,079 games, deliberately limiting the amount of available human gameplay data. We present baseline results for both two- and three- player Hanabi scenarios. To ensure fair evaluation, we host the proxy agents through a controlled evaluation system rather than releasing them publicly. The code is available at \href{https://github.com/FLAIROx/ah2ac2}{https://github.com/FLAIROx/ah2ac2}. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_21490 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Ad-Hoc Human-AI Coordination Challenge Dizdarević, Tin Hammond, Ravi Gessler, Tobias Calinescu, Anisoara Cook, Jonathan Gallici, Matteo Lupu, Andrei Muglich, Darius Forkel, Johannes Foerster, Jakob Nicolaus Artificial Intelligence Human-Computer Interaction Multiagent Systems Achieving seamless coordination between AI agents and humans is crucial for real-world applications, yet it remains a significant open challenge. Hanabi is a cooperative card game featuring imperfect information, constrained communication, theory of mind requirements, and coordinated action -- making it an ideal testbed for human-AI coordination. However, its use for human-AI interaction has been limited by the challenges of human evaluation. In this work, we introduce the Ad-Hoc Human-AI Coordination Challenge (AH2AC2) to overcome the constraints of costly and difficult-to-reproduce human evaluations. We develop \textit{human proxy agents} on a large-scale human dataset that serve as robust, cheap, and reproducible human-like evaluation partners in AH2AC2. To encourage the development of data-efficient methods, we open-source a dataset of 3,079 games, deliberately limiting the amount of available human gameplay data. We present baseline results for both two- and three- player Hanabi scenarios. To ensure fair evaluation, we host the proxy agents through a controlled evaluation system rather than releasing them publicly. The code is available at \href{https://github.com/FLAIROx/ah2ac2}{https://github.com/FLAIROx/ah2ac2}. |
| title | Ad-Hoc Human-AI Coordination Challenge |
| topic | Artificial Intelligence Human-Computer Interaction Multiagent Systems |
| url | https://arxiv.org/abs/2506.21490 |