1. Introduction

The term artificial intelligence (AI) was coined over 20 years ago but the endeavor to emulate human intelligence, in this case is how humans think, has been around since the 19th century. Why AI? Humans have intelligence that is not possessed by other living things. With this intelligence, humans can do almost everything, such as walking, talking, creating something worthy, doing business, leading an organization, inventing new technologies, and writing papers. How can humans do these things? There has to be something awesome inside that motivates humans to perform such actions. There will be no action if there is no knowledge, because it is something awesome that produces knowledge. It was then found that this something awesome is the brain with its complex structure as well as its mechanism. Without a brain, humans are just a pile of useless skin and bones. It is like a computer without a processor. So, the brain is the source of all human actions. The brain is the only “engine” that processes all information perceived by humans and the processed information becomes knowledge that creates human intelligence, namely, the ability to do almost everything.

Humans appear to do almost anything by orchestrating all of their apparatuses easily to perform the actions mentioned previously. What we can do is to emulate how the brain works by processing information to obtain knowledge. The brain works when humans think and this process is automatic when humans observe or see phenomena around them, meaning that there is a computation mechanism within the brain that will benefit humankind if it can be emulated in computer‐based systems. There have been many approaches, techniques, and methods invented by many researchers all over the world. Some approaches are based on how the brain's neural networks work, use of stored knowledge or past learning, how humans think or act rationally, and the human infomation processing system. It is understood that computation within the human brain is very complex and cannot be emulated exactly 100%. Therefore, emulating some of its magnificence is something that can benefit humankind.

Based on the information in the previous paragraph, the first approach is called computational AI [1], the second is called knowledge‐based AI or machine learning [2], the third according to [3] is called the agent approach, while the fourth according to [4] is called the Human Inference System (HIS) approach. Since the introduction of agent terminology as well as its definition by [3, 5], any kind of approach in AI is related to the agent because it is assumed to represent humans in real life. To become intelligent, an agent has to have knowledge and if we look at humans, an agent can obtain knowledge by any number of methods. With humans, knowledge does not come suddenly without process. Therefore, how can we get knowledge of something? First, we have to have information regarding that thing. This information is then sent to the brain to be processed to become knowledge of such thing. Based on this time knowledge, we can estimate the recognition of such thing. The knowledge of such thing can be added to or grown if we get more information from other sources. The more information we get, the more knowledge we have to more accurately estimate the recognition of a thing. Estimating is making a decision about something that might happen in the future. Estimation can only be done through thinking.

In this scheme of a real‐life mechanism, we can see that the process of getting knowledge not only shows how neural networks work (as an example), namely, processing the information and how to store the knowledge in the brain's memory for later use, but also shows how the information, as the first source of knowledge, is gathered and processed within the brain with the ultimate aim of having comprehensive knowledge. This knowledge starts from nothing, grows with the increasing amount and kinds of information as well as the kinds of information source, and is stored and extracted when decisions need to be made. If other researchers focus their research on how the brain's cells work or how to create an intelligent agent with certain characteristics, we focus our research on how humans obtain their knowledge with a mechanism known as information extraction.

Our research was initiated from our curiosity that there has to be a unique mechanism within the brain that gives humans knowledge. It is an abstract thing but it is real. Therefore, we had a hypothesis that the brain does something that we call growing the knowledge. The knowledge will grow with the accretion of information as time passes. This is the essential matter of the system called the Knowledge‐Growing System (KGS), a kind of cognitive agent. This system has been around since its first introduction in 2009 [6]. The aim of this chapter is as follows. Section 1 will give a brief introduction on what KGS is and a glance at AI. The approach perspectives to the development of KGS will be delivered in Section 2. In Section 3 we will show the steps needed to build a KGS. Examples of the utilization of KGS to real‐world problems will be given in Section 4. Section 5 will provide some concluding remarks.