Qualitative reasoning for system reconstruction using Lebesgue discretization

System reconstruction refers to the following problem: given a behaviour system, viewed as an overall system, determine which sets of its subsystems are adequate for reconstructing the given system with an acceptable degree of approximation. The reconstruction problem can be solved by finding multiple variable relationships in complex data. To deal with this problem, we apply the idea of qualitative reasoning as discussed in artificial intelligence. The basic idea is to convert continuous data into discrete data (e.g. through clustering) so that qualitative rules can be constructed. In this paper a method of compressing data (called the Lebesgue discretization) is proposed to establish relationships between the dependent variable and other variables. Qualitative rules can be constructed from this relationship and then stored in a knowledge base. This technique also facilitates automated knowledge acquisition.