I think James Taylor and my good friend Paul Vincent should be mindful not to reduce CEP (accidentally or intentionally) to rule-based systems, and broaden their perspectives and blog entries. In the original work on CEP by Dr. Luckham, the point of CEP is to solve complex problems in many problem domains, many require backwards chaining, uncertainty principles, statistical methods and more. Rule-based systems are interesting and useful, congruent with expert-systems, but also have well documented limitations (see notes below) in the classes of complex problems they can efficiently address.
Both James and Paul have excellent backgrounds in rule-based systems and have worked together in this area; on the other hand, CEP is not simply “rules and events” or “rules with EDA” etc.
Dr. Luckam’s background as a distinquished professor at Stanford was AI, including debugging large-scale distributed systems and performing complex network security research for DARPA. In all of these application areas, there is a known limit to the usefulness of rule-based approaches to address complex classes of decision support systems that require statistical methods to mitigate uncertainty. Rule-based systems are very useful, but they are suboptimal for the challenges of more complex decision support services that are better addressed by statistical and stochastic methods designed for systems with uncertainty – the problem set addressed by Dr. Luckam’s original CEP work.
I enjoy reading James and Paul supporting each other in the area of rules-based approaches to CEP; but I hope the “business rules folks” will keep in mind that CEP was designed to be significantly broader than rule-based decision support.
Reference 1: Rule-based systems are only feasible for problems for which any and all knowledge in the problem domain can be written in the form of if-then rules and for which this problem space is not large.
Reference 2: Abstract: “We demonstrate that classes of dependencies among beliefs held with uncertainty cannot be represented in rule-based systems in a natural or efficient manner. We trace these limitations to a fundamental difference between certain and uncertain reasoning. In particular, we show that beliefs held with certainty are more modular than uncertain beliefs. We argue that the limitations of the rule-based approach for expressing dependencies are a consequence of forcing non-modular knowledge into a representation scheme originally designed to represent modular beliefs….”
Reference 3: “Rule based systems have no ability to automatically learn from their mistakes, nor do they have any way of determining information from their environment. As such, their use is usually limited to very simple problems that have a finite, known set of possible states.”
Reference 4: Broad Google search on limitations of rule-based systems.