Intelligent vehicles (IVs) have gained worldwide attention due to their increased
convenience, safety advantages, and potential commercial value. Despite predictions of …

Planning under uncertainty is critical to robotics. The partially observable Markov decision
process (POMDP) is a mathematical framework for such planning problems. POMDPs are …

A broad introduction to algorithms for decision making under uncertainty, introducing the
underlying mathematical problem formulations and the algorithms for solving them …

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

Autonomous vehicle (AV) technology can provide a safe and convenient transportation
solution for the public, but the complex and various environments in the real world make it …

The significant contribution of human errors, accounting for approximately 94%(with a
margin of±2.2%), to road crashes leading to casualties, vehicle damages, and safety …

This paper surveys the field of deep multiagent reinforcement learning (RL). The
combination of deep neural networks with RL has gained increased traction in recent years …

Automated driving requires decision making in dynamic and uncertain environments. The
uncertainty from the prediction originates from the noisy sensor data and from the fact that …

Online solvers for partially observable Markov decision processes have been applied to
problems with large discrete state spaces, but continuous state, action, and observation …

Social cognition depends on our capacity for 'mentalizing', or explaining an agent's
behaviour in terms of their mental states. The development and neural substrates of …