@unpublished{ZaeffererGaidaBartzBeielstein2014,
  author    = {Zaefferer, Martin and Gaida, Daniel and Bartz-Beielstein, Thomas},
  title     = {Multi-fidelity Modeling and Optimization of Biogas Plants},
  url       = {http://nbn-resolving.de/urn:nbn:de:hbz:832-cos-644},
  year      = {2014},
  abstract  = {An essential task for operation and planning of biogas plants is the optimization of substrate feed mixtures. Optimizing the monetary gain requires the determination of the exact amounts of maize, manure, grass silage, and other substrates. Accurate simulation models are mandatory for this optimization, because the underlying chemical processes are very slow. The simulation models themselves may be time-consuming to evaluate, hence we show how to use surrogate-model-based approaches to optimize biogas plants efficiently. In detail, a Kriging surrogate is employed. To improve model quality of this surrogate, we integrate cheaply available data into the optimization process. Doing so, Multi-fidelity modeling methods like Co-Kriging are employed. Furthermore, a two-layered modeling approach is employed to avoid deterioration of model quality due to discontinuities in the search space. At the same time, the cheaply available data is shown to be very useful for initialization of the employed optimization algorithms. Overall, we show how biogas plants can be efficiently modeled using data-driven methods, avoiding discontinuities as well as including cheaply available data. The application of the derived surrogate models to an optimization process is shown to be very difficult, yet successful for a lower problem dimension.},
  subject      = {Biogas},
  language  = {en}
}
@unpublished{BagheriThillKochetal.2014,
  author    = {Bagheri, Samineh and Thill, Markus and Koch, Patrick and Konen, Wolfgang},
  title     = {Online Adaptable Learning Rates for the Game Connect-4},
  url       = {http://nbn-resolving.de/urn:nbn:de:hbz:832-cos-704},
  year      = {2014},
  abstract  = {Das Erlernen von Brettspielen durch Spiele eines Computers gegen sich selbst hat eine lange Tradition in der K{\"u}nstlichen Intelligenz. Basierend auf Tesauro's herausragendem Erfolg mit TD-Gammon in 1994, nutzen viele erfolgreiche selbstlernende Computerprogramme f{\"u}r Brettspiele heute Temporal Difference Learning (TDL). Um jedoch erfolgreich zu sein, muss man die betrachteten Merkmale sorgf{\"a}ltig ausw{\"a}hlen und oft viele Millionen Trainingsspiele absolvieren. In dieser Arbeit untersuchen wir Varianten zu TDL, insbesondere Incremental Delta Bar Delta (IDBD) und Temporal Coherence Learning (TCL), ob sie das Potential besitzen, wesentlich schneller zu lernen. Wir schlagen eine neue TCL-Variante mit geometrischer Schrittweite vor und vergleichen diese mit verschiedenen anderen Schrittweiten-Adaptionsverfahren aus dem Stand der Technik. Wir zeigen am Beispiel des Brettspiels "Vier Gewinnt" (Connect-4), dass Algorithmen mit geometrischer Schrittweite deutlich (um den Faktor 4) schneller lernen als Standard-TDL-Verfahren.},
  subject      = {Maschinelles Lernen},
  language  = {en}
}