There was an interesting discussion on LinkedIn recently about Expert Systems versus Business Rules (and, my contribution, versus CEP). This was posted on the BPTrends discussion board by business rules guru Ron Ross – probably not coincidentally as not only are business rules and decisions related to business processes, of course, but also one of the better business books on Expert Systems in the 1980s was by one Paul Harmon (presumably the one and same Paul Harmon who runs BPTrends… ).
Expert systems were in-vogue in the 1980′s as decision support systems. The term was most popular in the US, as I recall, whereas the Europeans preferred the more defensible term “knowledge based systems”. Nonetheless, expert systems were usually given a set of criteria, not least of which was the first:
- expert levels of performance (comparable to a human expert)
- ability to explain its reasoning
- ability to learn new knowledge.
Technologically, these tended to use features like CBR , or inference rule engines that were backward-chaining (goal-driven) - they tried to prove relevant hypotheses through questions and answers. Usually they were connected only to a console to support interactive “conversations” with end-users.
Such decision-support systems were ideal for technical support call centers, where the operators interacted with the expert system on behalf of callers. This also had the side-effect of training the operators. Indeed, one call center (if I recall correctly) found that after some months use, all its expert system operators were qualifiable as “experts” in the specialist domain they were dealing with!
Now fast forward 2+ decades, and we find ourselves dealing with vast amounts of information and events, automating and optimising business systems to save costs and improve performance. Here we see parallels with between expert systems and CEP implementations:
- best available expert knowledge encoded from the available Subject Matter Experts
- take expert event-driven decisions (and, if needed, log the rules used for monitoring offline or in a dashboard)
- use rules to determine whether goal states are achieved…
This is a long way from the interactive expert systems of old, but nonetheless shows similarities in both use cases (e.g. providing expert decision-making) and technologies (e.g. TIBCO BusinessEvents uses at its core an inference rule engine of the type used in many such expert systems).
I don’t advocate resurrecting the term “expert system”, even though I see an ex-colleague sports the job title of “Director of Expert Systems” at a well-known media company! But “expert performance” – obtaining results equivalent or better to what a human expert would achieve – is still something to strive for.
I’m interested in that discussion you mention.
Unfortunately the LinkedIn interface refuses access unless I’m a member then fails to tell me what the group is. (Interesting design!)
Could you tell me what LinkedIn group that is. (I’ve requested membership of the Complex Event Processing User Group but it’s not an “instant join” group.)
The term Expert System rolls nicely off the tongue but even in it’s hey day, covered such diversity as to be close to meaningless. These days I see all sorts of applications of the term making it’s meaning even more diffuse.
In some domains, with narrowly defined questions, matching “acknowledged experts” is not hard. It’s fairly easy to trounce most of them. Simply put in many areas the experts aren’t.
The focus on “logic programming” hides the fact that there are other approaches. Some of which “just work” in areas where logical inference fails badly.
Hi Mike – sorry I didn’t give more details on the discussion – it was actually on the BP Trends group.
“These days I see all sorts of applications of the term making it’s meaning even more diffuse.”
I’m surprised. I don’t come across the term at all!
The focus on “logic programming” hides the fact that there are other approaches. Some of which “just work” in areas where logical inference fails badly.
You mean, like analytics/statistical methods or things like neural networks (good luck proving the embedded rules in the latter are always compliant with things like discrimination legislation, mind you!). So sure there are other approaches, but logic and inference is still pretty good!
Cheers