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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
