本論文主要係探討足球機器人行為選擇策略之設計與研究。在機器足球員競賽中，控制策略為球員角色和行為選擇的核心，主導了球隊的勝敗。本論文設計了一個完整的策略系統，由球場上狀況獲知球員和球所在位置，以決定各球員需擔任角色。各角色再根據球所在之區域選擇其行為，並以模糊控制器來調整速度。接著，本論文嘗試藉由基因調控網路的觀念來進行策略設計，由基因間相互抑制和活化的概念，建構動態方程式，而方程式中的參數可依球員和球間的關係作動態地調整，以進行角色之派任。最後，運用FIRA五對五機器人足球賽模擬器來驗證所提出方法的效益及適用性。

　　A strategy system called two-stage strategy is proposed in the paper. In this thesis, the control strategy design of robot soccer game is mainly conferred. The control strategy contains role assignment of each robot and its behavior selection, which lead the team into victory or defeat. We first get position information of robots and ball from the situations of game field, and then resolve each robot that which role should play. Next, according to the role and the region where the ball is, the role’s behavior should be determined, and the speed of robot’s behavior is regulated by the fuzzy logic controller. In order to obey the game rules, some roles are unable to enter some specified regions. Then the numbers of robots in the designated range should be controlled. Furthermore, we attempt to apply the concept of genetic regulatory network in the role assignment mechanism. From the consideration of genes inhibition and activation, we construct a dynamic equation to select the roles, and the parameters of the equation are dynamically regulated from the relations of ball and robots. Finally, the 3D Robot Soccer Simulator of the Middle League SimuroSot of the FIRA is adopted to verify the feasibility and effectiveness of the proposed schemes.

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