Deep Learning-Based Hypothesis Generation Model and Its Application on Virtual Chinese Calligraphy-Writing Robot

In recent years, a tremendous amount of effort has been devoted to modeling the cognition of human brain, particularly hypothesis generation process. Most research of the hypothesis generation model is probability-based. However, computation of human brains is still neuron-based instead of calculating the probability. As an attempt to solve this problem in this paper, we propose a novel neuron-based hypothesis generation model, called hypothesis generation net, to model human cognition, including how to make decisions and how to do actions. Basically, the proposed hypothesis generation model consists of two parts, i.e., a hypothesis model and an evaluation model. When these two models interact, the system is able to generate hypotheses to solve complex tasks based on historical experiences. To validate the feasibility of the proposed hypothesis generation model, we show a virtual robot with its cognition system can learn how to write Chinese calligraphy in a simulation environment, where an image-to-action translation via a cognitive framework is proposed to learn the pattern of Chinese characters. Based on the proposed deep thinking and learning mechanism, the virtual robot is able to write Chinese calligraphy well, which is a difficult task requiring extremely complicated motions, through thinking and practicing according to a human writing sample.