Markov decision processes (MDPs) are often used to model sequential decision problems
involving uncertainty under the assumption of centralized control. However, many large …

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 …

Decision-theoretic planning is a popular approach to sequential decision making problems,
because it treats uncertainty in sensing and acting in a principled way. In single-agent …

Multi-agent settings in the real world often involve tasks with varying types and quantities of
agents and non-agent entities; however, common patterns of behavior often emerge among …

Multiagent decision-making in partially observable environments is usually modelled as
either an extensive-form game (EFG) in game theory or a partially observable stochastic …

Recent superhuman results in games have largely been achieved in a variety of zero-sum
settings, such as Go and Poker, in which agents need to compete against others. However …

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 …

Online, sample-based planning algorithms for POMDPs have shown great promise in
scaling to problems with large state spaces, but they become intractable for large action and …

We propose an online algorithm for planning under uncertainty in multi-agent settings
modeled as DEC-POMDPs. The algorithm helps overcome the high computational …

Distributed problem solving is a subfield within multiagent systems, where agents are
assumed to be part of a team and collaborate with each other to reach a common goal. In …