We focus on resilience in cooperative multi-agent systems, where agents can change their
behavior due to udpates or failures of hardware and software components. Current state-of …

Value function factorization (VFF) is a popular approach to cooperative multi-agent
reinforcement learning in order to learn local value functions from global rewards. However …

Objectively quantifying the value of player actions in football (soccer) is a challenging
problem. To date, studies in football analytics have mainly focused on the attacking side of …

State-of-the-art multi-agent reinforcement learning has achieved remarkable success in
recent years. The success has been mainly based on the assumption that all teammates …

Enabling autonomous agents to act cooperatively is an important step to integrate artificial
intelligence in our daily lives. While some methods seek to stimulate cooperation by letting …

State-of-the-art approaches to partially observable planning like POMCP are based on
stochastic tree search. While these approaches are computationally efficient, they may still …

One of the challenging problems in sensor network systems is to estimate and track the state
of a target point mass with unknown dynamics. Recent improvements in deep learning (DL) …

Decision making in multi-agent systems (MAS) is a great challenge due to enormous state
and joint action spaces as well as uncertainty, making centralized control generally …

A characteristic of reinforcement learning is the ability to develop unforeseen strategies
when solving problems. While such strategies sometimes yield superior performance, they …

Engineering intelligent industrial systems is challenging due to high complexity and
uncertainty with respect to domain dynamics and multiple agents. If industrial systems act …