| Summary: | 博士 === 國立交通大學 === 資訊管理研究所 === 93 === 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.
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