“If you need to go beyond gathering [i.e., harvesting or mining] business rules to trying to understand “The Knowledge” [sic] that experts know, how experts think and decide, what the expert rules are, or what the higher-level heuristic rules are, then knowledge engineers … will keep using “knowledge acquisition” (KA), “knowledge representation” (KR), and “knowledge engineering” (KE). AI guys know that means.”
In other words, expert rules arise from an individual who is outstanding at his particular knowledge task. That’s very different. Expert Systems Wikipedia describes expert systems as follows:“software that uses a knowledge base of human expertise for problem solving, or to clarify uncertainties where normally one or more human experts would need to be consulted … a traditional application and/or subfield of artificial intelligence (AI)”
Bob Whyte, a practitioner for a major insurance company, makes the following observation about the difference between business rules and expert systems[1]:“What makes the real-world challenge of managing business rules so much more tractable than it appeared to academics and researchers in the1980s, the heyday of knowledge engineering and expert systems, is that in the day-to-day business world the institution plays role of ‘god’.
… for business rules the problem is not one of having to discover and define hidden, unknown or unexpressed rules, which takes you into byzantine solution spaces, but rather one of documenting known rules invented overtly and explicitly by actual historical person(s).
With business rules you are generally not discovering rules no one has ever consciously considered, but rather uncovering rules that some manager, lawyer or other expert decided on one day, but probably did not record simply for lack of an appropriate infrastructure for rulebook management.”
Excellent clarification!