activity
20182025
most citedTheory of Mind for Multi-Agent Collaboration via Large Language Models

73 citations · 107 across the 11 of their papers we have counts for

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6 papers · 1 filter

cs.LG2023

Personalized Decision Supports based on Theory of Mind Modeling and Explainable Reinforcement Learning

Huao Li, Yao Fan, Keyang Zheng +2

In this paper, we propose a novel personalized decision support system that combines Theory of Mind (ToM) modeling and explainable Reinforcement Learning (XRL) to provide effective…

cs.LG2023

On the Role of Emergent Communication for Social Learning in Multi-Agent Reinforcement Learning

Seth Karten, Siva Kailas, Huao Li +1

Explicit communication among humans is key to coordinating and learning. Social learning, which uses cues from experts, can greatly benefit from the usage of explicit communication…

cs.LG2022★ 9 cited

Interpretable Learned Emergent Communication for Human-Agent Teams

Seth Karten, Mycal Tucker, Huao Li +3

Learning interpretable communication is essential for multi-agent and human-agent teams (HATs). In multi-agent reinforcement learning for partially-observable environments, agents…

cs.LG2021★ 7 cited

Emergent Discrete Communication in Semantic Spaces

Mycal Tucker, Huao Li, Siddharth Agrawal +4

Neural agents trained in reinforcement learning settings can learn to communicate among themselves via discrete tokens, accomplishing as a team what agents would be unable to do al…

cs.LG2020★ 3 cited

Predicting Human Strategies in Simulated Search and Rescue Task

Vidhi Jain, Rohit Jena, Huao Li +4

In a search and rescue scenario, rescuers may have different knowledge of the environment and strategies for exploration. Understanding what is inside a rescuer's mind will enable…

cs.LG2018★ 13 cited

Transparency and Explanation in Deep Reinforcement Learning Neural Networks

Rahul Iyer, Yuezhang Li, Huao Li +3

Autonomous AI systems will be entering human society in the near future to provide services and work alongside humans. For those systems to be accepted and trusted, the users shoul…