Some learning techniques in hierarchical censored production rules (HCPRs) system

Abstract

This paper discusses learning techniques based upon the hierarchical censored production rules (HCPRs) system of knowledge representation. These HCPRs are written in the form: “A IF B UNLESS C GENERALITY G SPECIFICITY S,” where symbol A represents the conclusion, B is the set of preconditions, C is the set of exception conditions, G is the general information, while S represents the specific information. Learning can be classified into two major categories: the first includes the restructuring or modification of existing knowledge, and the second covers the creation of new knowledge depending upon externally supplied information and already acquired knowledge. In this system, schemes which modify various belief factors and information relegated to various operators (like IF, UNLESS, etc.) of an HCPR fall in the first category, while schemes which create a new HCPR in the system by using externally supplied information and already acquired knowledge fall in the second category. Using the growth algorithm, a new HCPR is added in the system by maintaining consistency as well as minimizing redundancy. The set of all related HCPRs connected to the SPECIFICITY or GENERALITY operators are shown to possess a tree structure, and hence it is given the name HCPRs tree. The fission algorithm restructures an HCPRs tree, thereby enabling the system to reorganize its knowledge base; a new HCPR may be created during this process. This is followed by the fusion algorithm that enables the merging of two related HCPRs trees in the HCPRs system. © 1998 John Wiley & Sons, Inc.