Reconfigurable devices offer the ability to program electronic circuits on demand. In this
work, we demonstrated on-demand creation of artificial neurons, synapses, and memory …

Reinforcement learning (RL) approaches that combine a tree search with deep learning
have found remarkable success in searching exorbitantly large, albeit discrete action …

Planning robot dexterity is challenging due to the non-smoothness introduced by contacts,
intricate fine motions, and ever-changing scenarios. We present a hierarchical planning …

Recognising the goals or intentions of observed vehicles is a key step towards predicting the
long-term future behaviour of other agents in an autonomous driving scenario. When there …

Online solvers for partially observable Markov decision processes have difficulty scaling to
problems with large action spaces. Monte Carlo tree search with progressive widening …

This paper introduces Voronoi Progressive Widening (VPW), a generalization of Voronoi
optimistic optimization (VOO) and action progressive widening to partially observable …

The ability to plan ahead efficiently is key for both living organisms and artificial systems.
Model-based planning and prospection are widely studied in cognitive neuroscience and …

In this study, we introduce the Framework for Optimized Customizable User-Informed
Synthesis (FOCUS), a generative machine learning model tailored for drug discovery …

Abstract Monte-Carlo Tree Search (MCTS) is largely responsible for the improvement not
only of many computer games, including Go and General Game Playing (GPP), but also of …

In this paper, we present a novel learnable and continuous Monte-Carlo Tree Search
method, named as KB-Tree, for motion planning in autonomous driving. The proposed …