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We argue that these constraints help dimensioning the CR decision making engine.
Learning can consequently help the decision making engine to improve the quality of future decisions.
The captured -and/or computed- metrics by the sensors are then processed by the decision making engine.
We oppose it to rational thinking where the decision making engine always aims at the most rewarding option.
And thus, it enables them to improve the performance of their decision making engine considering a given uncertainty level.
We denote by CR design space an abstract three dimensional space that characterizes the CR decision making engine as shown in Figure 4.
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An interesting illustration can be found in article [52] in the case of GAs based decision making engines.
Such decision making engines would probably need to interact with environment to test the candidates and confirm their performance.
Moreover, a fuzzy decision-making engine was developed to make the system more powerful for attack detection, using the fuzzy inference approach.
This method provides a test engineer with key insights into the software's decision-making engine and how those decisions affect transitions between performance modes.
Government and opposition MPs sit on opposing benches behind their leaders, the two pistons of the decision-making engine.
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