This paper offers an active inference account of choice behaviour and learning. It focuses on
the distinction between goal-directed and habitual behaviour and how they contextualise …

Lubrication Condition monitoring (LCM) is not only utilized as an early warning system in
machinery but also, for fault diagnosis and prognosis under condition-based maintenance …

State-transition modeling (STM) is an intuitive, flexible, and transparent approach of
computer-based decision-analytic modeling, including both Markov model cohort simulation …

We consider a robust approach to address uncertainty in model parameters in Markov
decision processes (MDPs), which are widely used to model dynamic optimization in many …

OBJECTIVE: In the modern healthcare system, rapidly expanding costs/complexity, the
growing myriad of treatment options, and exploding information streams that often do not …

Active inference is a first principle account of how autonomous agents operate in dynamic,
nonstationary environments. This problem is also considered in reinforcement learning, but …

The aim of this work was to develop and evaluate the reinforcement learning algorithm
VentAI, which is able to suggest a dynamically optimized mechanical ventilation regime for …

As in other domains, artificial intelligence is becoming increasingly important in medicine. In
particular, deep learning-based pattern recognition methods can advance the field of …

Deep Reinforcement Learning (DRL) algorithms can scale to previously intractable
problems. The automation of profit generation in the stock market is possible using DRL, by …

How can risk analysts help to improve policy and decision making when the correct
probabilistic relation between alternative acts and their probable consequences is …