标题: 以认知学习修正XCS建构具知识教育与机械学习之双模式学习机制—以财务资料预测之知识学习为例
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: 陈怡璋
Yi-Chang Chen
陈安斌
An-Pin Chen
资讯管理研究所
关键字: 人工智慧;心理学;认知结构;试误学习;教育学习;智慧型学习模型;Artificial Intelligence;Psychology;Cognition Structure;Trial and Error;Teaching-Base Education;Intelligence-Learning Model
公开日期: 2004
摘要: 自1956年以来,人工智慧所定义的机器学习与长久以来研究人类心智行为的心理学所定义的学习,两者明显不同。由于电脑运算能力的提升,使得我们可以有能力再次重新检视学习的定义,以此希望可以达成更高效率与准确率的智慧学习模型。
本研究企图以认知心理学之认知结构来修正自1956年以来人工智慧之发展,由于人工智慧长期局限于试误学习之低效率学习模式,然而试误学习于传统心理学定义中仅限于刺激与反应之经验行为而已,由此学习模式所建构之任何机器学习,均只能认定为经验之适应模式而已,而较进阶的种类,如演化式计算模型,也只是其能透过电脑强大的运算能力来达成所谓的动态环境下之演化式学习模式,其中演化之特色只是多考虑了外在环境的变化或内在参数的调整,而整个学习流程却没有进ㄧ步修正。这也可说明,当各人工智慧之原始模型发展针对封闭式环境问题,都会有很好的表现,但面对非封闭式问题时,却只能经由大量实验和透过参数的调整来片面获取结果而无法自圆其说。
认知心理学中较完整的认知学习发展是在1986年以后,相关研究指出有效率的学习过程必须包含教育学习,而不再仅有透过试误学习来达成。以此,本研究发展ㄧ套修正传统机器学习之学习流程-双模式智慧型学习机制。另外,由于XCS系统是试误学习类之效果较佳及准确率较好的其中之ㄧ模型,透过以XCS为基础加上本研究所提之学习流程架构,继而发展出一个有效率之智慧型学习模型(E&R-R model)。
最后,本研究试图以较复杂的问题来进行实验模拟,而该问题为运用财务资料以建立财务预测知识模型,其模式为三种:XCS,R-R XCS与 E&R-R XCS,透过三种模型的准确率与最后报酬率之比较来验证本研究所提出之学习流程的效能。初步验证,E&R-R XCS均较R-R XCS 和XCS之机制有显着效能提升。
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
显示于类别:Thesis


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