Preference Learning for Move Prediction and Evaluation Function Approximation in Othello

This paper investigates the use of preference learning as an approach to move prediction and evaluation function approximation, using the game of Othello as a test domain. Using the same sets of features, we compare our approach with least squares temporal difference learning, direct classification, and with the Bradley-Terry model, fitted using minorization-maximization (MM). The results show that the exact way in which preference learning is applied is critical to achieving high performance. Best results were obtained using a combination of board inversion and pair-wise preference learning. This combination significantly outperformed the others under test, both in terms of move prediction accuracy, and in the level of play achieved when using the learned evaluation function as a move selector during game play

EVOLUTIONARY PROBLEM SOLVING

This thesis on evolutionary problem solving is a summation of work accomplished over a period of four years. My interest in this field was aroused when unintentionally attending an IEEE workshop on genetic algorithms in 1994. There are numerous people to thank for making this work possible: First of all Magnus Thor Jonsson for his encouragement, and creating the liberty to pursue this area of research. Xin Yao for sharing his expertise and guidance. Einar Arnason for endless discussions on evolutionary processes and natural systems. John Stuart for his patience in discussing the nature of representations. Gudmundur R. Jonsson for helping with difficult statistics. Jörgen Pind for renewing my interest in connectionist models. Mikael Karlson for making me doubt everything. Ruhul Sarker for generating interest in constrained problems. Hans-Paul Schwefel and David B. Fogel for sharing their insight into the working principles of evolutionary computation. Hans-George Beyer for discussions on theories for evolution strategies. Richard Sutton for pointing out the problems with evolutionary computation. Finally, I thank the Department of Mechanical Engineering and my many friends there for creating an invigorating academic environment

Continuous selection and self-adaptive evolution strategies

Abstract-The intention of this work is to eliminate the need for a synchronous generation scheme in the (p $ A) evolution strategy. It is motivated by the need for a more practical implementation of selection strategies on parallel machine architectures. This strategy is known as continuous or steady state selection. Continuous selection is known to reduce significantly the number of function evaluations needed to reach an optimum, in evolutionary search, for some problems. Here evolution strategy theory is used to illustrate when continuous selection is more efficient than generational selection. How this gain in efficiency may influence the overall effectiveness of the evolution strategy is also investigated. The implementation of continuous selection becomes problematic for algorithms using explicitly encoded selfadaptive strategy parameters. Self-adaption is therefore given special consideration in this work. The discussion leads a new evolution strategy version. I

Detekce hroznového vína v reálných podmínkách s využitím SVM klasifikátoru

The detection of grapes in real scene images is a serious task solved by researches dealing with precision viticulture. The detection of wine grapes of red varieties is a well mastered problem; however, the detection of white varieties still poses challenges. In this paper, four detectors for white wine grapes detection are introduced and evaluated. The detectors are based on support vector machines and they differ in kernels and features used for image representation. Namely, the pixel intensities and histogram of oriented gradients (HOG) are used for the representation of images. Radial basis functions and linear kernels are applied. The detectors based on the HOG feature have proven to be very efficient. Their average recognition accuracy by cross-validation was 98.23% and 98.96%, respectively. Furthermore, they show very good performance for other cross-validation metrics. Their average precision is 0.978 and 0.985, respectively; their average recall is 0.987 and 0.994, respectively. The detectors were also verified on test sets with positive samples affected by rotation distortion, and moreover on image sections of a real scene photo, in both cases with good results. Moreover, the detectors do not require any artificial lighting and they can work under different light conditions.V článku jsou představeny čtyři detektory hroznového vína v reálných fotkách. Tyto detektory jsou založeny na Suppor Vector Machine (SVM) klasifikátoru. Detektory využívají různých deskriptorů (intenzita pixelů v černobílém obraze a Histogram Orientovaných Gradientů (HOG)) a různých jádrových funkcí (lineární a Gaussova). Detektory založené na HOG deskriptoru vykazují při křížové validaci velmi vysokou přesnost a to 98,23% pro lineární jádrovou funkci a 98,96% pro Gausovu. Ostatní metriky rovněž vykazují při křížové validaci vysoké hodnoty. Detektory byly ohodnoceny i s využitím specializovaných testovacích sad a aplikovány na výřezy reálné fotografie. I v těchto případech s velmi dobrými výsledky. Představené řešení nevyžaduje umělé osvětlení