Knowledge engineering is a field of AI that tries to emulate the judgment and behaviour of a human expert in a given field.
Expert systems involve a large and expandable knowledge base, integrated with a rules engine that specifies how to apply information to each particular situation. The systems may also incorporate machine learning so that they can learn from experience in the same way that humans do. Expert systems are used in various fields including healthcare, customer service, financial services, manufacturing and law.
One famous example of successful knowledge engineering was built by Google and defeated the world's best ‘Go’ player in 2017. ‘Go’ is an abstract strategy game, invented in China, and is arguably humankind’s most complicated board game.
AlphaGo, Google’s AI was designed to study different playbooks, changing its strategy after every move. It won by a narrow margin of 0.5 points. Google say AlphaGo was designed to win, so its margin of success was simply a stopping point. It didn’t need to win by anything else. The project was an example of how Artificial Intelligence can be programmed to think like humans.
Using algorithms to emulate the thought patterns of a subject matter expert, knowledge engineering tries to take on questions and issues as a human expert would. Looking at the structure of a task or decision, knowledge engineering studies how the conclusion is reached. A library of problem-solving methods and a body of collateral knowledge are used to approach the issue or question. Depending on the task and the knowledge that is drawn on, the virtual expert may assist with troubleshooting, solving issues, assisting a human or acting as a virtual agent.