AMAN: Agent for Mentoring and Assisting Newbies in MMORPG
Jeehyun Lee, Seung-Moo Yang, Won Ik Cho
Abstract
In online games with diverse contents and frequent updates, newcomers first learn gameplay mechanics by community intelligence but soon face challenges that require real-time guidance from senior gamers. To provide easy access to such support, we introduce AMAN, Agent for Mentoring and Assisting Newbies in MMORPG (Massively Multiplayer Online Role-Playing Game) - a companion chatbot designed to engage novice gamers. Our model functions as a human-like chat buddy that interacts with users in a friendly manner while providing substantive informational depth. In this light, we propose a multi-stage learning approach that incorporates continual pre-training with a sequence of online resources and instruction tuning on curated dialogues. To align with gamers’ specific needs, we first analyze user-oriented topics from online communities regarding a widely played MMORPG and construct a domain-specific dataset. Furthermore, we develop a multi-turn dialogue data to foster dynamic conversations with users. The evaluation result with the model trained upon publicly available language model shows our practical applicability on how conversational assistant in online games can help novice gamers.
BibTeX
@inproceedings{lee-etal-2025-aman,
title = "{AMAN}: Agent for Mentoring and Assisting Newbies in {MMORPG}",
author = "Lee, Jeehyun and
Yang, Seung-Moo and
Cho, Won Ik",
editor = "Rambow, Owen and
Wanner, Leo and
Apidianaki, Marianna and
Al-Khalifa, Hend and
Eugenio, Barbara Di and
Schockaert, Steven and
Darwish, Kareem and
Agarwal, Apoorv",
booktitle = "Proceedings of the 31st International Conference on Computational Linguistics: Industry Track",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.coling-industry.45/",
pages = "522--532"
}