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.
This paper also proposes a Japanese-to-Braille translation system which uses the proposed AKB. Experimental results have showed that the case acquisition and similarity weight adjustment can reduce errors, and that the threshold adjustment significantly reduces segmentation errors.
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Ono, S., Yamasaki, T. & Nakayama, S. Adaptive knowledge base with attribute weight and threshold adjustments for Japanese-to-Braille translations. Artif Life Robotics 12, 59–64 (2008). https://doi.org/10.1007/s10015-007-0442-z
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DOI: https://doi.org/10.1007/s10015-007-0442-z