73 citations · 107 across the 11 of their papers we have counts for
13 papers
Adaptively Coordinating with Novel Partners via Learned Latent Strategies
Benjamin Li, Shuyang Shi, Lucia Romero +7
Adaptation is the cornerstone of effective collaboration among heterogeneous team members. In human-agent teams, artificial agents need to adapt to their human partners in real tim…
Modeling Latent Partner Strategies for Adaptive Zero-Shot Human-Agent Collaboration
Benjamin Li, Shuyang Shi, Lucia Romero +7
In collaborative tasks, being able to adapt to your teammates is a necessary requirement for success. When teammates are heterogeneous, such as in human-agent teams, agents need to…
Language Grounded Multi-agent Reinforcement Learning with Human-interpretable Communication
Huao Li, Hossein Nourkhiz Mahjoub, Behdad Chalaki +5
Multi-Agent Reinforcement Learning (MARL) methods have shown promise in enabling agents to learn a shared communication protocol from scratch and accomplish challenging team tasks.…
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…
Theory of Mind for Multi-Agent Collaboration via Large Language Models
Huao Li, Yu Quan Chong, Simon Stepputtis +4
While Large Language Models (LLMs) have demonstrated impressive accomplishments in both reasoning and planning, their abilities in multi-agent collaborations remains largely unexpl…
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…
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…
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…
Adaptive Agent Architecture for Real-time Human-Agent Teaming
Tianwei Ni, Huao Li, Siddharth Agrawal +6
Teamwork is a set of interrelated reasoning, actions and behaviors of team members that facilitate common objectives. Teamwork theory and experiments have resulted in a set of stat…
Hiding Leader's Identity in Leader-Follower Navigation through Multi-Agent Reinforcement Learning
Ankur Deka, Wenhao Luo, Huao Li +2
Leader-follower navigation is a popular class of multi-robot algorithms where a leader robot leads the follower robots in a team. The leader has specialized capabilities or mission…
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…
Explanation of Reinforcement Learning Model in Dynamic Multi-Agent System
Xinzhi Wang, Huao Li, Hui Zhang +2
Recently, there has been increasing interest in transparency and interpretability in Deep Reinforcement Learning (DRL) systems. Verbal explanations, as the most natural way of comm…
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…