ILISCE: A system for learning control heuristics in a scheduling environment

Abstract

This paper presents a general learning structure called ILISCE which learns meta-rules to guide a scheduling system. The scheduling program, called OPAL, solves real-world problems using a set of local heuristics which incrementaly resolve the whole set of conflicts among the physical resources of the floor. A Selective Inductive learning approach is used to create concepts representing typical states of the job shop which allow a Classifier to "recognize" current situations and to choose the corresponding best operators to apply on. In this paper, we present the general architecture of ILISCE.