When collecting and curating data we need to pay close attention to decision-elasticity — how different would our information have to be to change our optimal action?
This analysis will help to design an optimal action plans in order to mitigate the faults effects.
Optimal action selection chooses the controller design that makes the closed-loop behavior as close as possible to the desired one.
These EDA-based agents select their optimal action plan by considering the occupants' thermal sensations, their behavioural adaptations and the energy consumption of the HVAC system.
In this problem, the insured has to consider the so-called optimal action rather than the contracted compensation (or indemnity) due to the existence of the bonus.
The proposed research work utilizes the Q-learning algorithm to train the system for the optimal action selection strategy during the state-action learning process by updating the action values based on the obtained rewards.
This fuzzy-based decision support (FDS) model adopts the fuzzy reasoning approach to suggest the optimal action that needs to be taken to deal with the problem of asset conditions.
To study how the brain solves such action selection problems, we designed a Multi Feature Sorting Task in which the task rules defining an optimal action have a hierarchical structure and studied concurrent brain activity using it.
We present a model of hierarchical curiosity loops for such an autonomous active learning agent, whereby each loop selects the optimal action that maximizes the agent's learning of sensory-motor correlations.
In order to provide guidance during such activities, CogWatch needs to plan which optimal action should be done by users at each step of the task, and detect potential errors in their behavior.
However, none of the instruments insures compliance at the individual level, and as a result hefty fines may be accrued by individuals even when they choose the socially optimal action.
