Noisy sensing, imperfect control, and environment changes are defining characteristics of
many real-world robot tasks. The partially observable Markov decision process (POMDP) …

In stochastic power systems, electric vehicle (EV) fast charging stations (FCS) are rapidly
being installed, while adversely impacts the distribution network. Due to this, the improper …

Robotics has dramatically increased our ability to gather data about our environments. This
is an opportune time for the robotics and algorithms community to come together to …

Multi-Agent Active Search (MAAS) is an active learning problem with the objective of
locating sparse targets in an unknown environment by actively making data-collection …

Robotics has dramatically increased our ability to gather data about our environments,
creating an opportunity for the robotics and algorithms communities to collaborate on novel …

Making decisions in complex environments is a key challenge in artificial intelligence (AI).
Situations involving multiple decision makers are particularly complex, leading to …

The active search for objects of interest in an unknown environment has many robotics
applications including search and rescue, detecting gas leaks or locating animal poachers …

Multi-agent active perception is a task where a team of agents cooperatively gathers
observations to compute a joint estimate of a hidden variable. The task is decentralized and …

In this article, we consider the computational and communication challenges of partially
observable multiagent sequential decision-making problems. We present algorithms that …

We present recursive small-step multi-agent A∗(RS-MAA∗), an exact algorithm that
optimizes the expected reward in decentralized partially observable Markov decision …