Search Results for author:
Found 10 papers, 3 papers with code
CBGT-Net: A Neuromimetic Architecture for Robust Classification of Streaming Data
This paper describes CBGT-Net, a neural network model inspired by the cortico-basal ganglia-thalamic (CBGT) circuits found in mammalian brains.
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.
Concept Learning for Interpretable Multi-Agent Reinforcement Learning
Multi-agent robotic systems are increasingly operating in real-world environments in close proximity to humans, yet are largely controlled by policy models with inscrutable deep neural network representations.
Explainable Action Advising for Multi-Agent Reinforcement Learning
This allows the student to self-reflect on what it has learned, enabling advice generalization and leading to improved sample efficiency and learning performance - even in environments where the teacher is sub-optimal.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
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.
Deep Interpretable Models of Theory of Mind
When developing AI systems that interact with humans, it is essential to design both a system that can understand humans, and a system that humans can understand.
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.
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.
Embedded Neural Networks for Robot Autonomy
We present a library to automatically embed signal processing and neural network predictions into the material robots are made of.
Distributed Machine Learning in Materials that Couple Sensing, Actuation, Computation and Communication
As the ultimate goal of this research is to incorporate the approaches described in this survey into a robotic material paradigm, the potential for adapting the computational models used in these applications, and corresponding training algorithms, to an amorphous network of computing nodes is considered.
