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    <abstract>My research interests lie in human-agent collaboration and user adaptation, with a particular emphasis on adaptation in real-time collaborative environments.The field of collaborative systems aims to support human teams in completing complex tasks efficiently while ensuring natural and adaptive interaction experiences.I investigate how AI agents can function as effective partners by adapting to their human collaborators.A central focus of my research is the personalization of agent behavior based on user proficiency.This includes methods for adapting the agent’s communication strategies according to the user’s skill level and task experience. To pursue this goal, I collected and analyzed a multimodal dataset of human-human interaction using a real-time collaborative cooking game environment (Wu et al., 2021; Liu et al., 2024).The chosen environment is characterized by its complex task mechanics and strict time constraints, which necessitate seamless coordination and elicit dynamic, natural collaborative behaviors such as role negotiation and error recovery.Through this analysis, I investigated how partners with different levels of task proficiency communicate and coordinate effectively.Based on the findings, I proposed practical design guidelines for future adaptive AI agents, enabling them to adjust their level of guidance and initiative in response to the user’s proficiency.</abstract>
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%0 Conference Proceedings
%T Towards Adaptive Human-Agent Collaboration in Real-Time Environments
%A Nakae, Kaito
%Y Whetten, Ryan
%Y Sucal, Virgile
%Y Ngo, Anh
%Y Chalamalasetti, Kranti
%Y Inoue, Koji
%Y Cimino, Gaetano
%Y Yang, Zachary
%Y Zenimoto, Yuki
%Y Rodriguez, Ricardo
%S Proceedings of the 21st Workshop of Young Researchers’ Roundtable on Spoken Dialogue Systems
%D 2025
%8 August
%I Association for Computational Linguistics
%C Avignon, France
%F nakae-2025-towards
%X My research interests lie in human-agent collaboration and user adaptation, with a particular emphasis on adaptation in real-time collaborative environments.The field of collaborative systems aims to support human teams in completing complex tasks efficiently while ensuring natural and adaptive interaction experiences.I investigate how AI agents can function as effective partners by adapting to their human collaborators.A central focus of my research is the personalization of agent behavior based on user proficiency.This includes methods for adapting the agent’s communication strategies according to the user’s skill level and task experience. To pursue this goal, I collected and analyzed a multimodal dataset of human-human interaction using a real-time collaborative cooking game environment (Wu et al., 2021; Liu et al., 2024).The chosen environment is characterized by its complex task mechanics and strict time constraints, which necessitate seamless coordination and elicit dynamic, natural collaborative behaviors such as role negotiation and error recovery.Through this analysis, I investigated how partners with different levels of task proficiency communicate and coordinate effectively.Based on the findings, I proposed practical design guidelines for future adaptive AI agents, enabling them to adjust their level of guidance and initiative in response to the user’s proficiency.
%U https://aclanthology.org/2025.yrrsds-1.5/
%P 12-14

Markdown (Informal)

[Towards Adaptive Human-Agent Collaboration in Real-Time Environments](https://aclanthology.org/2025.yrrsds-1.5/) (Nakae, YRRSDS 2025)

• Towards Adaptive Human-Agent Collaboration in Real-Time Environments (Nakae, YRRSDS 2025)

ACL

• Kaito Nakae. 2025. Towards Adaptive Human-Agent Collaboration in Real-Time Environments. In Proceedings of the 21st Workshop of Young Researchers' Roundtable on Spoken Dialogue Systems, pages 12–14, Avignon, France. Association for Computational Linguistics.