案例式推理（Case-Based Reasoning: CBR）是近年在人工智慧領域中廣為大家所討論及應用的議題之一。在案例式推理的流程中，為了增加系統的推理能力往往透過保留案例的步驟將推論過的案例保留進案例庫。然而隨著案例庫案例的擴張，將造成系統在擷取案例時，計算相似度上的負擔，導致系統擷取效率下降的問題。本研究認為案例式推理起源於人類的認知推理過程，當人類遭遇問題時，往往從自身過去經驗中找尋類似事件、情境，進而利用過去的解決方案對目前的問題進行決策。然而人類並不會因為經驗的累積導致在記憶搜尋上速度顯著下降。因此本研究從認知心理學觀點切入探討人類的記憶結構，進而重建案例式推理中的案例庫，並且提出兩階段案例推理模式，改善案例式推理中案例擷取隨著案例數增加而減緩擷取速度的問題，進而增進推論效率。

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中文部分
方榮吉(民91)。以案例式推理建構主機板製程分析系統，台北科技大學生產系統工程與管理研究所論文。
王麒瑋（民92）。支向機核心函數是用指標之建立，成功大學工業與資訊管理研究所論文。
林志明(民88)。應用案例式推理、灰色關聯分析法發展產品設計評估決策支援系統，中央大學工業管理研究所碩士論文。
柯文達(民91)。校園意外事件處理程序之案例式研究，東華大學教育研究所論文。
陳一傑(民86)。應用模糊理論於多專家案例式推理之研究，元智大學工業工程研究所論文。
黃弘毅(民90)。設計概念階段之產品知識建構與應用-以案例式推理法為例，東海大學工業設計所論文。
鄭昭明(民82)。認知心理學,桂冠,台北。
鄭麗玉(民82)。認知心理學,五南,台北。

英文部分
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