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 …

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 …

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

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 …

It is essential for users to understand what their AI systems can and can't do in order to use
them safely. However, the problem of enabling users to assess AI systems with sequential …

Value Decomposition (VD) aims to deduce the contributions of agents for decentralized
policies in the presence of only global rewards, and has recently emerged as a powerful …

Most approaches for goal recognition rely on specifications of the possible dynamics of the
actor in the environment when pursuing a goal. These specifications suffer from two key …

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 …

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 …

Ad hoc teamwork (AHT) refers to the problem of enabling an agent to collaborate with
teammates without prior coordination. State of the art methods in AHT are data-driven, using …