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

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.