Populating the Expert System
Several methods exist for a knowledge engineer to obtain knowledge. One option is to go through textbooks and professional journals with the intent to extract definitions, axioms, and rules that apply to the issue. This type of knowledge acquisition is especially useful for teaching and reference situations because question-response paths are direct. However, how the question is posed to the expert system can lead to misleading results. Another method of acquiring knowledge is to ask human experts to explain their thought process and method for solving problem scenarios, sometimes referred to as verbal protocol analysis. Whereas, alternatively, a human expert can enhance the information obtained from literary resources and often bring unpublished knowledge, gained through experience, to the decision process paths. As a result, this combinational knowledge makes human-based expert systems a valuable technology.
To incorporate human expert knowledge into a technology-based expert system, the right individuals must be identified and selected. Specialists tend to be trained in rather narrow domains and are best at solving problems within their defined domains. Assuming experts do exist and are willing to participate; good experts are those who are able to solve particular types of problem scenarios that most others cannot solve with the same efficiency and/or effectiveness. Additionally, considerable time can be saved in developing an expert compliance system if the knowledge engineer has experience in the area being modeled.
After experts have been selected, the knowledge engineer must take the expert knowledge and transform it into a computational model. However, issues may arise because an expert discovers that they are unable to describe how a situational scenario is resolved. Typically, this is due to experts operating at a subconscious-level while performing some tasks to address a scenario. Considering the possibility of undefined steps generating misaligned logic paths in the inference engine, commonly, interdisciplinary teams of specialists must work in unison to formulate deductive reasoning processes for defined problems.