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Lanxiao Huang

8 accepted papers

2025

From Capabilities to Performance: Evaluating Key Functional Properties of LLM Architectures in Penetration Testing

EMNLP 2025

Large Language Models (LLMs) have been explored for automating or enhancing penetration testing tasks, but their effectiveness and reliability across diverse attack phases remain open questions. This study presents a comprehensive evaluation of multiple LLM-based agents, ranging from singular to mod

Cited by 0SourcePDFScholar
2024

Enhancing Human Experience in Human-Agent Collaboration: A Human-Centered Modeling Approach Based on Positive Human Gain

ICLR 2024poster

Existing game AI research mainly focuses on enhancing agents' abilities to win games, but this does not inherently make humans have a better experience when collaborating with these agents. For example, agents may dominate the collaboration and exhibit unintended or detrimental behaviors, leading to…

Cited by 2SourcePDFScholar
2023

Hokoff: Real Game Dataset from Honor of Kings and its Offline Reinforcement Learning Benchmarks

NeurIPS 2023poster

The advancement of Offline Reinforcement Learning (RL) and Offline Multi-Agent Reinforcement Learning (MARL) critically depends on the availability of high-quality, pre-collected offline datasets that represent real-world complexities and practical applications. However, existing datasets often fall…

2023

Towards Effective and Interpretable Human-Agent Collaboration in MOBA Games: A Communication Perspective

ICLR 2023top-25%

MOBA games, e.g., Dota2 and Honor of Kings, have been actively used as the testbed for the recent AI research on games, and various AI systems have been developed at the human level so far. However, these AI systems mainly focus on how to compete with humans, less on exploring how to collaborate wit…

Cited by 9SourcePDFScholar
2022

Honor of Kings Arena: an Environment for Generalization in Competitive Reinforcement Learning

NeurIPS 2022accept

This paper introduces Honor of Kings Arena, a reinforcement learning (RL) environment based on the Honor of Kings, one of the world’s most popular games at present. Compared to other environments studied in most previous work, ours presents new generalization challenges for competitive reinforcement…

2022

MCS: An In-battle Commentary System for MOBA Games

COLING 2022main

This paper introduces a generative system for in-battle real-time commentary in mobile MOBA games. Event commentary is important for battles in MOBA games, which is applicable to a wide range of scenarios like live streaming, e-sports commentary and combat information analysis. The system takes real…

Cited by 1SourcePDFScholar
2021

Learning Diverse Policies in MOBA Games via Macro-Goals

NeurIPS 2021poster

Recently, many researchers have made successful progress in building the AI systems for MOBA-game-playing with deep reinforcement learning, such as on Dota 2 and Honor of Kings. Even though these AI systems have achieved or even exceeded human-level performance, they still suffer from the lack of po…

Cited by 12SourcePDFScholar
2020

Towards Playing Full MOBA Games with Deep Reinforcement Learning

NeurIPS 2020poster

MOBA games, e.g., Honor of Kings, League of Legends, and Dota 2, pose grand challenges to AI systems such as multi-agent, enormous state-action space, complex action control, etc. Developing AI for playing MOBA games has raised much attention accordingly. However, existing work falls short in handli…