The emergence of the Internet of things and the widespread deployment of diverse
computing systems have led to the formation of heterogeneous multi-agent systems (MAS) …

This book presents an overview of formal decision making methods for decentralized
cooperative systems. It is aimed at graduate students and researchers in the fields of …

Decentralized partially observable Markov decision processes (Dec-POMDPs) are a
powerful tool for modeling multi-agent planning and decision-making under uncertainty …

Decentralized partially observable Markov decision processes (Dec-POMDPs) provide a
general model for decision-making under uncertainty in decentralized settings, but are …

This chapter presents an overview of the decentralized POMDP (Dec-POMDP) framework. In
a Dec-POMDP, a team of agents collaborates to maximize a global reward based on local …

This paper investigates the use of model-based reinforcement learning in the context of ad
hoc teamwork. We introduce a novel approach, named TEAMSTER, where we propose …

Despite a recent increase of research activity in the field of multi-agent planning there is still
no de-facto standard for the description of multi-agent planning problems similarly to the …

Abstract Partially Observable Markov Decision Processes (POMDPs) are powerful models
for planning under uncertainty in partially observable domains. However, computing optimal …

Dec-POMDPs are a powerful framework for planning in multiagent systems, but are provably
intractable to solve. Despite recent work on scaling to more agents by exploiting weak …

This article presents the state-of-the-art in optimal solution methods for decentralized
partially observable Markov decision processes (Dec-POMDPs), which are general models …