The term "rule-based systems" seems to represent a technology in search of a definition. In a strict sense, there is no such thing as rule-based systems as a subject area - it is a set tools and methods borrowed from several different academic disciplines and subjects. The areas that have contributed the most to rule-based systems are artificial intelligence and cognitive science. There are also a larger number other specialized areas such as fuzzy logic, case-based reasoning, induction and rule discovery which may or may not included within the higher level definitions of the parent subjects, that is artificial intelligence and cognitive science. Sometimes it is difficult to know where one subject ends and the next begins.
One hazy area is "expert systems", which to this day forms the basis of many definitions of rule-based systems. Expert systems mimic the inner workings of a recognized expert in a field in order to break through a 'knowledge bottleneck' of some sort, in other words to multiply the presence of an expert by simulating their reasoning process via a computer program. Expert systems have largely disappeared in the last 10 years for various reasons and seem to have been absorbed into cognitive science as expressed in efforts to develop a deeper understanding of expert reasoning, which may potentially be pattern-based rather than solely rule-based.
Undoubtedly, the area that has contributed most to the success of rule-based systems as a mainstream technology is business rules. The leading edge was often an initiative to capture and record critical business rules using a powerful methodology - this may or may not resulting in an actual computer application. Later, the following edge was often a large and expensive application of "business rules technology" to replace a behemoth mainframe application of the previous decades with more flexible and managable applications using rule engines. In large part, the technological tools employed for business rules was identical to that of expert system. In a sense, many business rule systems are the equivalent of non-expert rule-based systems, albeit on a larger 'enterprise' scale.
In either case, at the core of the technology is the idea of using a rule engine to externalize business or expert logic that would normally be implemented in ( buried in ! ) program code. This confers a competitive advantage ( a.k.a. 'agility' ) on the company utilizing the technology, resulting in better decisions, quicker time-to-market and better ability to respond to changing business opportunities. Under the auspices of business rules, rule-based systems that has started out as toy systems in the 1980s, grew into prototypes of business systems in the 1990s and then became the foundation of large-scale transaction processing applications over the last 10 years. It was a long struggle and took close to 20 years to accomplish.