CASE-BASED LEARNING FOR KNOWLEDGE-BASED OPTIMIZATION MODELING SYSTEM - UNIK-CASE

Abstract

This article explores how the previously built optimization models can be used as a medium for automatic learning. To explain the learning process, we describe the target representation of common modeling knowledge base in UNIK-OPT and the representations of specific optimization model cases. Both are represented in frames. The common modeling knowledge is represented by the potential linkability between attributes and indices, attributes and blocks of terms, and blocks of terms and constraints. There are also no fixed indices on blocks of terms and constraint sets. Therefore, the learning process includes the addition of new frames, generalization of linkability, generalization of attribute's role (constant or variable), and context identification in terms of period, time units, usage, perspectives, and so forth. To realize the learning process, the UNIK-CASE is under development as a front end the modeling system UNIK-OPT.