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

Statistical dialog systems (SDSs) are motivated by the need for a data-driven framework that
reduces the cost of laboriously handcrafting complex dialog managers and that provides …

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) …

Sequential decision making, commonly formalized as Markov Decision Process (MDP)
optimization, is an important challenge in artificial intelligence. Two key approaches to this …

Autonomous vehicles are expected to play a key role in the future of urban transportation
systems, as they offer potential for additional safety, increased productivity, greater …

We study the robustness of reinforcement learning (RL) with adversarially perturbed state
observations, which aligns with the setting of many adversarial attacks to deep …

Covering formulation, algorithms, and structural results, and linking theory to real-world
applications in controlled sensing (including social learning, adaptive radars and sequential …

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 …