@article{oai:nitech.repo.nii.ac.jp:00004367,
 author = {王, 勇 and 犬塚, 信博 and 石井, 直弘},
 issue = {5},
 journal = {人工知能学会論文誌 / 人工知能学会誌},
 month = {Sep},
 note = {Similarity metrics is a central issue in automatic reasoning system, especially in case-based reasoning and analogical reasoning. This paper proposes a method of similarity metrics by focusing the similarity of features of problems which are represented by frame knowledge expressions. For retrieving the most similar previous case and adapting it in solving new problem, similarity metrics are needed to provide the retrieving criterion and the information of adaptation. In our method, we assign a degree of similarity as a retrieving criterion between the features of new problem and past one, and using fuzzy integral to calculate the degree of similarity by comparing target frame which presents the features of new problem with source frame which presents the features of past problem. While calculating the degree of similarity, we also make a comparative frame which includes the information of matching source frame to target frame for adaptation. Finally we show some simulating results., application/pdf},
 pages = {778--785},
 title = {フレーム表現における類似性の判定},
 volume = {10},
 year = {1995},
 yomi = {イヌヅカ, ノブヒロ}
}