標題: 以認知學習修正XCS建構具知識教育與機械學習之雙模式學習機制—以財務資料預測之知識學習為例
Applying Cognitive Learning to Enhance XCS to Construct a Dual-Mode Learning Mechanism of Knowledge-Education and Machine-Learning— an Example of Knowledge Learning on Finance Prediction
作者: 陳怡璋
Yi-Chang Chen
陳安斌
An-Pin Chen
資訊管理研究所
關鍵字: 人工智慧;心理學;認知結構;試誤學習;教育學習;智慧型學習模型;Artificial Intelligence;Psychology;Cognition Structure;Trial and Error;Teaching-Base Education;Intelligence-Learning Model
公開日期: 2004
摘要: 自1956年以來,人工智慧所定義的機器學習與長久以來研究人類心智行為的心理學所定義的學習,兩者明顯不同。由於電腦運算能力的提升,使得我們可以有能力再次重新檢視學習的定義,以此希望可以達成更高效率與準確率的智慧學習模型。 本研究企圖以認知心理學之認知結構來修正自1956年以來人工智慧之發展,由於人工智慧長期侷限於試誤學習之低效率學習模式,然而試誤學習於傳統心理學定義中僅限於刺激與反應之經驗行為而已,由此學習模式所建構之任何機器學習,均只能認定為經驗之適應模式而已,而較進階的種類,如演化式計算模型,也只是其能透過電腦強大的運算能力來達成所謂的動態環境下之演化式學習模式,其中演化之特色只是多考慮了外在環境的變化或內在參數的調整,而整個學習流程卻沒有進ㄧ步修正。這也可說明,當各人工智慧之原始模型發展針對封閉式環境問題,都會有很好的表現,但面對非封閉式問題時,卻只能經由大量實驗和透過參數的調整來片面獲取結果而無法自圓其說。 認知心理學中較完整的認知學習發展是在1986年以後,相關研究指出有效率的學習過程必須包含教育學習,而不再僅有透過試誤學習來達成。以此,本研究發展ㄧ套修正傳統機器學習之學習流程-雙模式智慧型學習機制。另外,由於XCS系統是試誤學習類之效果較佳及準確率較好的其中之ㄧ模型,透過以XCS為基礎加上本研究所提之學習流程架構,繼而發展出一個有效率之智慧型學習模型(E&R-R model)。 最後,本研究試圖以較複雜的問題來進行實驗模擬,而該問題為運用財務資料以建立財務預測知識模型,其模式為三種:XCS,R-R XCS與 E&R-R XCS,透過三種模型的準確率與最後報酬率之比較來驗證本研究所提出之學習流程的效能。初步驗證,E&R-R XCS均較R-R XCS 和XCS之機制有顯著效能提升。
From 1956, the learning definitions of Artificial Intelligence and Psychology to human mind/behavior are obviously different. Owing to the rapid development of the computing power, we have potential to enhance the learning mechanism. This work tries to apply the learning process of the cognition structure defined in Cognitive Psychology to enhance or modify the development of AI, of which the learning models are almost based on trial and error style. However, this kind of learning style is definably given to the experience behavior of stimulus and response in Psychology. Thus, the relative AI models based on such style are design as an experience-adaptation system. For better ones, e.g. evolution-base algorithms, they belonged to the system with more powerful computing power to the dynamical environment. Even so, it was considered not only outside environment but also internal parameter tuning. As for the entire learning process, it has never been enhanced. That is, various original AI models are easily to be developed to their own close-form problem. To the unclose-form problems, their distinct results only come from huge amounts of experiments and tuning their model’s parameters. As the result, it is not easy to make clear for the explanation to why or how. The desirable cognitive learning of cognitive psychology is the development that has started since 1986. The relative literatures have pointed out that teaching-base education would increase the learning efficiency, but trial and error style is not sufficient to learning. That is the reason we enhance the AI learning process to develop a dual-perspective learning mechanism. Furthermore, since XCS is a better accuracy model of AI, we have applied it as a basement and involve the enhanced model proposed to develop an intelligence-learning model. Finally, this work is designed a test of the more complex problem, which is constructing a finance prediction knowledge model. By comparing to the accuracy and accumulative profit of XCS, R-R XCS and E&R-R XCS respectively, the results obtain the obvious outcome. That is, the proposed learning framework has enhanced the original mechanism.
URI: http://140.113.39.130/cdrfb3/record/nctu/#GT008734503
http://hdl.handle.net/11536/50446
Appears in Collections:Thesis


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