A Rule-based System (RBS) is a good system to get the answer of What, How, and Why questions from the rule base during inferencing. Answers and explanations are properly provided. The problem with RBS is that it can’t easily perform the knowledge acquisition process and it can’t update the rules automatically. Only the expert can update them, manually, by the support of a knowledge engineer. Moreover most researches in RBS concern more about the optimization of the existing rules than about generating new rules from them. Rule optimization, however, can not change the result of the inferencing, significantly, in term of knowledge coverage.
Ripple Down Rules (RDR) came up to overcome the major problem of expert systems: experts no longer always communicate knowledge in a specific context. RDR allows for extremely rapid and simple knowledge acquisition without the help of a knowledge engineer. The user does not ever need to examine the rule base in order to define new rules: the user only needs to be able to define a new rule that correctly classifies a given example, and the system can determine where the rule should be placed in the hierarchy. The limitation of RDR is the lack of powerful inference. Unlike RBS which is equipped with inference through forward and backward chaining, RDR seems to use Depth First Search (DFS) which lacks the flexibility of question answering and explanation accrued from powerful inference.
A Variable-Centered Intelligent Rule System (VCIRS) is proposed in this thesis. It hybridizes RBS and RDR. The system architecture is adapted from RBS and obtains advantages from RDR. This system organizes the rule base in a special structure so that easy knowledge building, powerful knowledge inferencing and evolutional improvement of system performance can be obtained at the same time. The term “Intelligent” in VCIRS stresses that it can “learn” to improve the system performance from the user during knowledge building (via value analysis) and refining (by rule generation).

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