Search Results for author:
Found 10 papers, 2 papers with code
Personalized Decision Supports based on Theory of Mind Modeling and Explainable Reinforcement Learning
Our proposed approach is agnostic to task environment and RL model structure, therefore has the potential to be generalized to a wide range of applications.
Long-Horizon Dialogue Understanding for Role Identification in the Game of Avalon with Large Language Models
We discuss the capabilities of LLMs to utilize deceptive long-horizon conversations between six human players to determine each player's goal and motivation.
Theory of Mind for Multi-Agent Collaboration via Large Language Models
While Large Language Models (LLMs) have demonstrated impressive accomplishments in both reasoning and planning, their abilities in multi-agent collaborations remains largely unexplored.
Interpretable Learned Emergent Communication for Human-Agent Teams
In human-agent teams tested in benchmark environments, where agents have been modeled using the Enforcers, we find that a prototype-based method produces meaningful discrete tokens that enable human partners to learn agent communication faster and better than a one-hot baseline.
Emergent Discrete Communication in Semantic Spaces
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 alone.
Adaptive Agent Architecture for Real-time Human-Agent Teaming
Previous human-human team research have shown complementary policies in TSF game and diversity in human players' skill, which encourages us to relax the assumptions on human policy.
SpeakingFaces: A Large-Scale Multimodal Dataset of Voice Commands with Visual and Thermal Video Streams
We present SpeakingFaces as a publicly-available large-scale multimodal dataset developed to support machine learning research in contexts that utilize a combination of thermal, visual, and audio data streams; examples include human-computer interaction, biometric authentication, recognition systems, domain transfer, and speech recognition.
Predicting Human Strategies in Simulated Search and Rescue Task
In our efforts to model the rescuer's mind, we begin with a simple simulated search and rescue task in Minecraft with human participants.
Explanation of Reinforcement Learning Model in Dynamic Multi-Agent System
The results show that verbal explanation generated by both models improve subjective satisfaction of users towards the interpretability of DRL systems.
Transparency and Explanation in Deep Reinforcement Learning Neural Networks
For those systems to be accepted and trusted, the users should be able to understand the reasoning process of the system, i. e. the system should be transparent.
