The rapid advancement of chat-based language models has led to remarkable progress in
complex task-solving. However, their success heavily relies on human input to guide the …

Both Minsky's" society of mind" and Schmidhuber's" learning to think" inspire diverse
societies of large multimodal neural networks (NNs) that solve problems by interviewing …

High-performing teams learn effective communication strategies to judiciously share
information and reduce the cost of communication overhead. Within multi-agent …

Communication is an effective mechanism for coordinating the behavior of multiple agents.
In the field of multi-agent reinforcement learning, agents can improve the overall learning …

Achieving a widely accepted definition of human intelligence has been challenging, a
situation mirrored by the diverse definitions of artificial intelligence in computer science. By …

Multi-agent Reinforcement Learning (MARL) has gained wide attention in recent years and
has made progress in various fields. Specifically, cooperative MARL focuses on training a …

High-performing teams learn intelligent and efficient communication and coordination
strategies to maximize their joint utility. These teams implicitly understand the different roles …

Most recent research on multiagent reinforcement learning (MARL) has explored how to
deploy cooperative policies for homogeneous agents. However, realistic multiagent …

Communication-based multiagent reinforcement learning (MARL) has shown promising
results in promoting cooperation by enabling agents to exchange information. However, the …

During dynamic social interaction, inferring and predicting others' behaviors through theory
of mind (ToM) is crucial for obtaining benefits in cooperative and competitive tasks. Current …