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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.

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，由於人工智慧长期局限於试误学习之低效率学习模式，然而试误学习於传统心理学定义中仅限於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。

Connectors are lines that can be anchored to particular places, called glue points , on the graphic object.

连接符，可固定在图形对象的特定的地方的线条。

Another way of putting it, too many agents have a say in each other's work, and bureaucratic rigor mortis sets in.

另一种表达是，太多的作用物在彼此的工作里都有话语权，官僚的僵尸开始抬头。