Abstract Monte Carlo Tree Search (MCTS) is a powerful approach to designing game-
playing bots or solving sequential decision problems. The method relies on intelligent tree …

Thompson sampling is an algorithm for online decision problems where actions are taken
sequentially in a manner that must balance between exploiting what is known to maximize …

We propose information-directed sampling--a new algorithm for online optimization
problems in which a decision-maker must balance between exploration and exploitation …

We propose information-directed sampling—a new approach to online optimization
problems in which a decision maker must balance between exploration and exploitation …

Anytime multi-agent path finding (MAPF) is a promising approach to scalable path
optimization in large-scale multi-agent systems. State-of-the-art anytime MAPF is based on …

Decision Trees are prominent prediction models for interpretable Machine Learning. They
have been thoroughly researched, mostly in the batch setting with a fixed labelled dataset …

A soft robot is a kind of robot that is constructed with soft, deformable and elastic materials.
Control of soft robots presents complex modeling and planning challenges. We introduce a …

Over the last 10 to 15 years, active inference has helped to explain various brain
mechanisms from habit formation to dopaminergic discharge and even modelling curiosity …

Markov decision processes (MDPs) provide a rich framework for planning under uncertainty.
However, exactly solving a large MDP is usually intractable due to the “curse of …

Conceptual design of mechanisms is a crucial part of achieving product innovation as
mechanisms perform the transmission and transformation of specific motions in the machine …