Quantum reinforcement learning: Comparing quantum annealing and gate-based quantum computing with classical deep reinforcement learning

In this paper, we present implementations of an annealing-based and a gate-based quantum computing approach for finding the optimal policy to traverse a grid and compare them to a classical deep reinforcement learning approach.We extended these three approaches by allowing for stochastic actions instead of deterministic actions and by...

ViTroVo: in vitro assembly search for in vivo adaptive operator guidance: An artificial intelligence framework for highly customised manufacturing

Product customisation is a topic of growing interest in Smart Manufacturing. Allowing customers to design intended products brings additional challenges to the manufacturing task, such as the increase in flexibility of the assembly theatre, the compilation of assembly instructions for possibly unique products, and stress-related risks for human...

Multi-agent reinforcement learning using simulated quantum annealing

With quantum computers still under heavy development, already numerous quantum machine learning algorithms have been proposed for both gate-based quantum computers and quantum annealers. Recently, a quantum annealing version of a reinforcement learning algorithm for grid-traversal using one agent was published. We extend this work based on...

Intelligence Augmentation for Urban Warfare Operation Planning using Deep Reinforcement Learning

Intelligence augmentation for urban warfare operation planning using deep reinforcement learning

The density, diversity, connectedness and scale of urban environments make military operations challenging. This paper shows that different artificial intelligence techniques can be combined to provide the commander with various form of intelligence augmentation and to support the decision making process. A warfare model has been developed where...

Using Relational Concept Networks for Explainable Decision Support

In decision support systems, information from many different sources must be integrated and interpreted to aid the process of gaining situational understanding. These systems assist users in making the right decisions, for example when under time pressure. In this work, we discuss a controlled automated support tool for gaining situational...