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Different decision-making strategies require different amounts of consumers' time, energy and attention.
Recent studies have revealed that experiencing a stressor can change decision-making strategies and outcomes [1] [5].
Third, we did not use standard tools to assess information needs and decision-making strategies.
In the smart industry philosophy, continuous monitoring of the machinery condition is crucial to follow up the decision-making strategy.
Firstly, if the decision-making strategy is not suitable, the accuracy will be reduced.
For this purpose, a hybrid decision-making strategy using both iFR and FFR can be applied.
Furthermore, we have also presented a new segment-based decision-making strategy for outcome prediction [ 22].
A major goal of this work is to formalize and implement a decision making strategy in order to achieve an intelligent task-oriented active vision system for a biped walking robot.
Such decision making strategies are generally called reinforcement learning.
To this end, a multi-objective solution methodology and a fuzzy decision making strategy are implemented to find and sort the Pareto-optimal solutions.
This paper describes a predictive decision making strategy for the multiple robotic systems.
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