Recent work in multi-agent intention scheduling has shown that enabling agents to predict
the actions of other agents when choosing their own actions may be beneficial. However …

The non-stationarity of the environment is a crucial challenge for competitive Multi-Agent
Reinforcement Learning (MARL) due to the constantly changing opponent policy. Existing …

Tight coordination is required for effective human-robot teams in domains involving fast
dynamics and tactical decisions, such as multi-car racing. In such settings, robot teammates …

Multi-agent systems is an evolving discipline that encompasses many different branches of
research. The long-standing Agents at Aberdeen (A 3) group undertakes research across …

For an agent in a multi-agent environment, it is often beneficial to be able to predict what
other agents will do next when deciding how to act. Previous work in multi-agent intention …

Intelligent agents, like those based on the popular BDI agent paradigm, typically pursue
multiple goals in parallel. An intention scheduler is required to reason about the possible …

BDI (Belief-Desire-Intention) agents represent a paradigm in artificial intelligence,
demonstrating proficiency in reasoning, planning, and decision-making. They offer a …