Knowledge engineering is the branch of artificial intelligence (AI) that focuses on developing decision-making rules that are applied to data. It is a branch of AI specifically developed to replicate human expertise in a specific topic as closely as possible. The goal of knowledge engineering is to transfer the expertise of a problem-solving human into a program to make the same decisions with the same data. In the initial stages of knowledge engineering, the focus was more on the transfer process of knowledge from a human expert to the software program, but it was later determined that it did not adequately capture the entire decision-making process of a human expert. Analogous reasoning, gut feeling, and nonlinear thinking play a role in decision making and knowledge engineering did not consider these factors in its initial form.
Today, knowledge engineering focuses on developing models that create systems that produce the same results if not even better results than a human expert. These models are being implemented in software that can make decisions similar to human experts including financial advisors and decision support software. The goal of knowledge engineering is to develop technology that could eventually outdo human experts and make better decisions than them. As technology advances, knowledge engineering is becoming more sophisticated and capable of handling complex decisions. It is expected that knowledge engineering will be a major part of decision making and other industries going forward.