Creating art is often viewed as a uniquely human endeavor. In this paper, we introduce a multi-conditional Generative Adversarial Network (GAN) approach trained on large amounts of human paintings to synthesize realistic-looking paintings that emulate human art. Our approach is based on the StyleGAN neural network architecture, but incorporates a custom multi-conditional control mechanism that provides fine-granular control over characteristics of the generated paintings, e.g., with regard to the perceived emotion evoked in a spectator. We also investigate several evaluation techniques tailored to multi-conditional generation.
In many online markets, we observe fierce competition and highly dynamic price adjustments. Competitors frequently adjust their prices to respond to changing market situations caused by competitors’ price adjustments. In this paper, we examine price response strategies within an infinite horizon duopoly where the competitor’s strategy has to be learned. The goal is to derive knowledge about the opponent’s pricing strategy in a self-adaptive way and to balance exploration and exploitation. Our models are based on anticipated price reaction probabilities and efficient dynamic programming techniques. We show that our approach works when played against unknown strategies. Further, we analyze the mutual interplay of our self-learning strategies as well as their tendencies to form a cartel when motivated accordingly. Moreover, we propose two extensions of our model to integrate risk aversion. Finally, we demonstrate the effectiveness of parallelization techniques to speed up the computation of strategies as well as their simulation.
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