適合平行之相似案例索引技術

Abstract

案例式推論是一種人工智慧上的問題解決方式，案例式推論解決的方法就像人類一樣，是利用過去的案例和經驗找出一個合適的解答以解決目前的問題，而不是像其它的人工智慧上的問題解決方式，必須在問題的情況和解決問題的方式之間找出所謂之整體性的關連，然後利用這些整體性的關連來找出問題的解決方式。CBR 能夠充分的利用過去的每一個案例和經驗中所包含的問題之情況和解決的方式，來解決問題。在以案例為基礎之推論中最關鍵性的工作就是如何能正確的找出過去之相似的案例。有許多的研究成果已經被提出來針對此一問題做處理。但是當案例知識庫中的案例數目很巨大時，就會影響到整個案例搜尋的速度。在這篇論文中我們提出了一個新的索引技術配合一個對應的案例搜尋演算法，應用於數量巨大的案例知識庫中搜尋相似的案例。新的索引技術和對應的案例搜尋演算法非常適合於平行化，而且經過平行化之後效能也獲得很好的改善。經由與相關索引技術的比較結果，證明我們所提出的索引技術有較優良的效能。

Case-based reasoning (CBR) is a methodology of problem-solving in artificial intelligence. Just like human being, CBR uses prior cases to find out suitable solution for the new problems. Unlike the others, CBR pays attention to the characteristics of each case. CBR can correctly take advantage of the situations and methods in former cases to solve problems. A critical task of CBR is to retrieve similar prior cases accurately and many researchers have proposed some useful technologies to handle such problem. However, increasingly larger number of cases influences the performance of retrieving similar cases for the large-scale CBR was seldom been discussed. In this thesis, the performance issue of large-scale CBR is discussed and a new indexing method, called bit-wise indexing method, and the corresponding efficient algorithms are proposed for retrieving the similar cases in large-scale CBR efficiently. The bit-wise indexing method and the corresponding algorithm can be easily parallelized and thus gets great performance improvement in case retrieving and similarity measuring. Some experiments are made for comparing the performance with other methods and the results show the performance of proposed method is admirable.