臺灣博碩士論文加值系統

電腦與網路在這世代極為普遍，任何時候都可藉由相關的活動打發時間，網路遊戲也因此而蓬勃發展，遊戲的互動方式因此由單機遊戲進化成多人線上遊戲；多元的遊戲類型，也吸引著玩家選擇適合自己的遊戲型態，較著名的遊戲類型有第一人稱射擊(First Person Shooter, FPS)、角色扮演(Role-Playing Game, RPG)、格鬥遊戲(Fighting Game)、動作即時戰略遊戲(Multiplayer Online Battle Arena, MOBA)與即時戰略遊戲(Real-time Strategy, RTS)等等。
上述的任一遊戲在進入線上與他人互動之前，都需要對遊戲的屬性與操作有一定的熟悉度，所以線上遊戲都內含單機的教學或是訓練模式，引導玩家更快的進入遊戲，如何讓訓練有趣且合乎玩家的需求，則是本論文要探討的問題。
即時戰略遊戲為本論文所研究的方向，主要分為四大部份：資源的採集、科技的進化、戰士類型的選擇與進攻的時機。基地產生工人(worker)持續地採集資源，並同時使用輪盤方式選擇科技的發展方向，依目前的科技狀態使用基因演算法(Genetic Algorithm, 以下統稱為GA)推演最佳戰士類型，而GA的結果也會回饋科技的發展，在兩者相互影響下，將使結果導向最佳策略，最終由當下所擁有的戰士能力總合，決定何時發動攻擊。

關鍵字：即時戰略、基因演算法

Computer technology and Internet are becoming more commonly used in this generation. On-line games are developing greatly because there are many activities related to computer technology and Internet can be done to help people to kill time at all times. Interaction mode for on-line games has been changed from playing alone to multiplayer. Multiple game-types attract players to choose what they are interested in. There are some most popular on-line games such as First Person Shooter (FPS), Role-Playing Game (RPG), Fighting Game, Multiplayer Online Battle Arena (MOBA) and Real-time Strategy (RTS)…etc.
Based on forenamed game-types, players must be familiar with the game features and control interfaces before start to interact with other players. All of on-line games include the stand-alone education or the training mode to lead players into the games faster. The purpose of this study is to investigate how to make players playing with fun and also meet their requirements during the training mode.
The research direction for this paper is to propose a balanced strategy of RTS games. The main four points of the research is resource collection, technology evolution, warrior selection and offensive opportunity. The command center produces workers to collect resources and chooses development technology by the roulette way. Based on the current status of technology, the paper uses Genetic Algorithm (GA) to deduce the best type of warrior. The outcome of Genetic Algorithm (GA) will give back to the technology development eventually. Under the conditions of mutual influence, the best strategy for optimal results comes out accordingly. Finally, the sum
up of all existed warriors’ capacity decides the attack time.

Keywords: Real-time Strategy (RTS) game, Genetic Algorithm (GA).

摘要
ABSTRACT
論文目錄
圖目錄
表目錄
第一章 緒論
1.1 研究動機與目的
第二章 相關研究
第三章 研究方法
3.1 系統架構
3.1 DSP-生產worker
3.2 DSP-科技發展與建造
3.2.1 科技發展
3.2.2 科技附加機率
3.3 DSP-戰士選擇與生產
3.3.1 基因演算法
3.3.2 rtGA染色體定義
3.3.3 rtGA 適應性函式
3.3.4 rtGA中止條件
3.3.5 rtGA進化演算
3.3.6 能力調適
3.4 DSP-進攻
第四章 實驗結果與討論
4.1 實驗環境
4.2 評估模型
4.3 系統實作與結果
4.3.1 難易度 - 簡單
4.3.2 難易度 - 普通
4.3.3 難易度 - 困難
4.3.4 難易度 - 極難
4.3.5 DCA
第五章 結論與未來展望
第六章 參考文獻
附錄

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