Recent advances in machine learning have led to growing interest in Explainable AI (xAI) to
enable humans to gain insight into the decision-making of machine learning models …

In real-world multi-agent systems, agents with different capabilities may join or leave without
altering the team's overarching goals. Coordinating teams with such dynamic composition is …

Ad hoc teamwork is the research problem of designing agents that can collaborate with new
teammates without prior coordination. This survey makes a two-fold contribution: First, it …

Centralized training with decentralized execution (CTDE) has achieved great success in
cooperative multi-agent reinforcement learning (MARL) in practical applications. However …

Ad hoc teamwork is the well-established research problem of designing agents that can
collaborate with new teammates without prior coordination. This survey makes a two-fold …

Multi-agent collaboration with Large Language Models (LLMs) demonstrates proficiency in
basic tasks, yet its efficiency in more complex scenarios remains unexplored. In gaming …

A desirable goal for autonomous agents is to be able to coordinate on the fly with previously
unknown teammates. Known as “ad hoc teamwork”, enabling such a capability has been …

The development of autonomous agents which can interact with other agents to accomplish
a given task is a core area of research in artificial intelligence and machine learning …

Plan recognition, activity recognition, and goal recognition all involve making inferences
about other actors based on observations of their interactions with the environment and …

Open ad hoc teamwork is the problem of training a single agent to efficiently collaborate with
an unknown group of teammates whose composition may change over time. A variable team …