本論文以柔性計算為基礎，發展出三種智慧型控制系統，包括自我探索式、多目標自我探索式及自我創造式智慧型控制系統。自我探索式系統包括三個模組：模糊類神經網路控制器、效能評估器及適應器。此系統能評估控制效果良窳，若效果不彰則啟動適應器。適應器乃模仿人類之適應行為以解決困難，具有反省、檢討及探索的能力。因此，系統可以調整本身的行為法則－模糊規則－以適應環境變化。其次，本研究發展出多目標自我探索式系統，包括四個模組：模糊類神經網路控制器、感測器、多目標適應器及促進器。此系統與前者功能類似，但多了促進器以改善系統本身效率。此系統除了有適應能力，也能在系統的控制效率低時，探尋新的行為法則，來提昇系統效率。最後，本研究提出具創造力的自我創造式系統，包括符號控制器、感知器、符號適應器及創造器。本系統除了具有如自我探索式系統的適應力，也擁有創造的能力。此系統的知識表示型態為符號規則，而不同於前兩者的模糊規則。創造器透過四階段的創造過程：創造目標、創造程序、創造力衡量及知識轉換，以產生具有創意的行為準則。前面兩個系統皆以機器人路徑規劃作為實例驗證，模擬結果顯示兩者的機器人皆能成功地避開障礙物而到達目的地，但多目標自我探索式系統的機器人能產生較短的行進軌跡。自我創造式系統以人工動物群體之模擬模式作為驗證實例，模擬結果顯示系統展現其良好的適應性與創造性能力。

This study develops three types of intelligent control systems, namely SEICS (Self-Exploration process based Intelligent Control System), mSEICS (multi-objec-tive Self-Exploration process based Intelligent Control System) and SCICS (Self- Creation process based Intelligent Control System). The first of these, the SEICS, consists of a fuzzy neural network controller, a performance evaluator and an adaptor. If the performance of SEICS is poor, the adaptor will be activated. The adaptor, which emulates human adaptive behaviors, has capacities for introspection, self-criticism and exploration. Using these it is able to adapt to various environments by adjusting its behavioral rules, i.e., the fuzzy rules. Secondly, this study extends the SEICS to mSEICS. This includes four modules, i.e., fuzzy neural network controller, receptor, m-adaptor and advancer. Besides through adaptability, the mSEICS can also improve the system’s efficiency through the use of the additional advancer module; however, this is more complicated than SEICS. Finally, this study proposes the SCICS model, which consists of a symbolic controller, a percepter, an s-adaptor and a creator, which has both adaptive and creative abilities. The creation procedure of the creator can produce novel behavioral rules. These consist of four stages, i.e., creative objectives, creative process, creativity measurement and knowledge transformation. The simula-tion results of robotic path planning application in both the SEICS and mSEICS models show that both robots can successfully reach the target, although the robotic trajectory of the mSEICS is shorter. Moreover, the simulations of animat colony demonstrate the adaptability and creativity of SCICS as well.

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