K(Knowledge)-net: building up and its dynamics |
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Authors: | Yongguang Zhang Masanori Sugisaka Lu Tang |
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Affiliation: | (1) Institute of Systems Science, Academia Sinica, #55 East Zhongguancun Road, Haidian district, Beijing, 100080, PR China;(2) Department of Electrical and Electronic Engineering, Oita University, Oita, Japan;(3) College of Electricity and Information, The Hunan University, Changsha, Hunan Province, PR China |
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Abstract: | The essence of intelligence is to use certain abilities to obtain knowledge, to use that knowledge, and to operate with that knowledge. New knowledge learned by a human is often related to old existing knowledge, and sometimes we could have more conceptual knowledge based on old knowledge. So, the knowledge in the brain exists in a related structural form, and this structure is dynamic, and therefore is evolvable. Based on the understanding of the real process of learning by a human being, we discuss how to make a model to describe the dynamic structure of knowledge. This model is also a principle of artificial brain design. Most of the knowledge a child learns is from natural language and perception information, and we define this as semantic knowledge. The model to describe the process and structure of knowledge growing in a network form is called a K-net. It is a dynamic network with two main dynamics: one is new knowledge added, and the other is aggregating knowledge existing in the network with some probability. Under these very natural conditions, we found that the network is originally a simple random net, and then some characteristics of a complex network gradually appear when more new knowledge is added and aggregated. A more interesting phenomenon is the appearance of a random hierarchical structure. Does this mean emergence? |
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Keywords: | Semantic knowledge Complex networks Small-world Scale-free Hierarchical organization |
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