Adaptive knowledge base with attribute weight and threshold adjustments for Japanese-to-Braille translations

Abstract

This paper proposes a method for generating an adaptive knowledge base (AKB) involving two knowledge representations: rule and case. Combining rules and cases makes it possible to solve problems accurately and quickly, and to acquire new cases from problem-solving results. In general case-based problem-solving methods, the similarity metric must be defined for each problem domain. In previous work using rules and cases, a threshold of negative case applications had to be adjusted. The proposed AKB does not require manual adjustment of the threshold and the similarity metric.