K(Knowledge)-net: building up and its dynamics

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?