Real-world sequential decision-making tasks are generally complex, requiring trade-offs
between multiple, often conflicting, objectives. Despite this, the majority of research in …

We study the multi-agent multi-armed bandit (MAMAB) problem, where agents are factored
into overlapping groups. Each group represents a hyperedge, forming a hypergraph over …

Cyber-physical systems revolve around context awareness, empowering objective-oriented
services, products and operations based on real data. Self-aware and self-control systems …

We study the problem of regret minimization in Multi-Agent Multi-Armed Bandits (MAMABs)
where the rewards are defined through a factor graph. We derive an instance-specific regret …

Multi-agent coordination is prevalent in many real-world applications. However, such
coordination is challenging due to its combinatorial nature. An important observation in this …

We investigate the problem of best arm identification in Multi-Agent Multi-Armed Bandits
(MAMABs) where the rewards are defined through a factor graph. The objective is to find an …

Wind farms suffer from so-called wake effects: when turbines are located in the wind
shadows of other turbines, their power output is substantially reduced. These losses can be …

Budget allocation for online advertising suffers from multiple complications, including
significant delay between the initial ad impression to the call to action as well as cold-start …

This paper describes AI-Toolbox, a C++ software library that contains reinforcement learning
and planning algorithms, and supports both single and multi agent problems, as well as …

We present a novel model-based algorithm, Cooperative Prioritized Sweeping, for sample-
efficient learning in large multi-agent Markov decision processes. Our approach leverages …