TY  - INPR
A1  - Zaefferer, Martin
A1  - Gaida, Daniel
A1  - Bartz-Beielstein, Thomas
T1  - Multi-fidelity Modeling and Optimization of Biogas Plants
N2  - 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.
T3  - CIplus - 2/2014 
KW  - Biogas
KW  - Simulation
KW  - Modellierung
KW  - Optimierung
KW  - Kriging
KW  - Biogas Plant
KW  - Simulation
KW  - Optimization
KW  - Surrogate Models
KW  - Multi-fidelity
KW  - Co-Kriging
Y1  - 2014
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:832-cos-644
ER  - 
TY  - INPR
A1  - Bagheri, Samineh
A1  - Thill, Markus
A1  - Koch, Patrick
A1  - Konen, Wolfgang
T1  - Online Adaptable Learning Rates for the Game Connect-4
N2  - Das Erlernen von Brettspielen durch Spiele eines Computers gegen sich selbst hat eine lange Tradition in der Künstlichen Intelligenz. Basierend auf Tesauro's herausragendem Erfolg mit TD-Gammon in 1994, nutzen viele erfolgreiche selbstlernende Computerprogramme für Brettspiele heute Temporal Difference Learning (TDL). Um jedoch erfolgreich zu sein, muss man die betrachteten Merkmale sorgfältig auswählen 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.
N2  - Learning board games by self-play has a long tradition in computational intelligence for games. Based on Tesauro’s seminal success with TD-Gammon in 1994, many successful agents use temporal difference learning today. But in order to be successful with temporal difference learning on game tasks, often a careful selection of features and a large number of training games is necessary. Even for board games of moderate complexity like Connect-4, we found in previous work that a very rich initial feature set and several millions of game plays are required. In this work we investigate different approaches of online-adaptable learning rates like Incremental Delta Bar Delta (IDBD) or Temporal Coherence Learning (TCL) whether they have the potential to speed up learning for such a complex task. We propose a new variant of TCL with geometric step size changes. We compare those algorithms with several other state-of-the-art learning rate adaptation algorithms and perform a case study on the sensitivity with respect to their meta parameters. We show that in this set of learning algorithms those with geometric step size changes outperform those other algorithms with constant step size changes. Algorithms with nonlinear output functions are slightly better than linear ones. Algorithms with geometric step size changes learn faster by a factor of 4 as compared to previously published results on the task Connect-4.
T3  - CIplus - 3/2014 
KW  - Maschinelles Lernen
KW  - Modelllernen
KW  - Adaptive Schrittweite
KW  - Reinforcement Learning
KW  - Temporal Difference Learning (TDL)
KW  - Board Games
KW  - Online Adaptation
KW  - N-tuple Systems
Y1  - 2014
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:832-cos-704
ER  -