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We illustrate our approach on a real-world analysis problem from the domain of mobile telephony.
Eighteen participants were confronted with a computer-based office simulation, and they had to work on a problem from the domain of business administration.
First, the participants were welcomed and told they would have to solve a problem from the domain of business administration using a computer-based office simulation.
We compare the result with that of the other clustering tools using a classic problem from the domain of group technology.
A well known example of this problem from the domain of reinforcement learning is the pole-balancing problem, but more recently, the work of [9] and [3] has applied a failure avoidance approach to the problem of consensus avoidance in networks.
Furthermore, we show that ProBMoT is able, in an automated fashion and using a combination of knowledge– and data-driven modeling, to solve a complex, relevant and challenging problem from the domain of systems biology.
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We perform an empirical evaluation of the implemented methods on three real-world modeling problems from the domain of population dynamics in aquatic ecosystems.
Our approach can be easily applied to a whole range of problems from the domain of protein engineering.
Interestingly, it turns out that almost every variant of RNA secondary structure prediction problem, as well as additional problems from the domain of CFGs, sustain the VMT requirements.
This family includes many important problems from the domains of RNA folding and CFG parsing.
Additional related problems from the domains of RNA folding and Context Free Grammars (CFGs) parsing fall under the VMT framework, and it is likely that D-discreteness can be exploited for accelerating the computation of more problems within this family.
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