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 …

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 …

A multi-agent partially observable Markov decision process (MPOMDP) is a modeling
paradigm used for high-level planning of heterogeneous autonomous agents subject to …

We consider the challenging problem of online planning for a team of agents to
autonomously search and track a time-varying number of mobile objects under the practical …

We consider the online planning problem for a team of agents to discover and track an
unknown and time-varying number of moving objects from onboard sensor measurements …

Many recent successful off-policy multi-agent reinforcement learning (MARL) algorithms for
cooperative partially observable environments focus on finding factorized value functions …

Multi-agent partially observable Markov decision processes (MPOMDPs) provide a
framework to represent heterogeneous autonomous agents subject to uncertainty and …

Human computation allows computer systems to leverage human intelligence in
computational processes. While it has primarily been used for tasks that are not time …

In centralized multi-agent systems, often modeled as multi-agent partially observable
Markov decision processes (MPOMDPs), the action and observation spaces grow …

Abstract Markov Decision Processes (MDPs) provide an extensive theoretical background
for problems of decision-making under uncertainty. In order to maintain computational …