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In this paper we design a generic framework for strategical negotiation of service level values under time constraints and exemplify the usage of our framework by extending the Bayesian learning agent to cope with the limited duration of a negotiation session.
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The overall user trends in user interactions was observed by the usage of our analytics framework in the CDS tool.
We also present a case study of our framework's usage.
We show various usage scenarios of our PDL and demonstrate the effectiveness of our framework for a commonly used scientific kernel and a financial application on different configurations of a state-of-the-art CPU/GPU system.
These achieved and presented experimental results are very encouraging for the performances of our framework, and work in favor of the intended usage of it.
Supplementary Figure 5 Extendibility of our framework.
Figure 2 Flow of our framework.
Fig. 2 Overview of our framework.
All confirms the efficiency of our framework.
"They can reduce undesirable side-effects and are a more efficient usage of our healthcare budget".
Figure 1 depicts examples of the usage of our algorithm.
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