Rating of discrimination networks for rule-based systems
Abstract
The amount of information stored in a digital form grows on a daily basis but is mostly only understandable by humans, not machines. A way to enable machines to understand this information is using a representation suitable for further processing, e. g. frames for fact declaration in a Rule-based System. Rule-based Systems heavily rely on Discrimination Networks to store intermediate results to speed up the rule processing cycles. As these Discrimination Networks have a very complex structure it is important to be able to optimize them or to choose one out of many Discrimination Networks based on its structural efficiency. Therefore, we present a rating mechanism for Discrimination Networks structures and their efficiencies. The ratings are based on a normalised representation of Discrimination Network structures and change frequency estimations of the facts in the working memory and are used for comparison of different Discrimination Networks regarding processing costs.
