當遊戲複雜度提高，策略遊戲面臨了許多的挑戰，這些挑戰同時也成為AI研究領域上的議題。因為龐大的遊戲狀態空間(game space)、動作空間(action spaces)，現存的搜尋演算法都無法在這麼巨大的搜尋空間中，並且時間有限的情況下做出一個好的選擇，嚴重的影響到AI的發展性、強度。除此之外，玩家對於AI的需求，也日漸提升。許多遊戲的AI在經過玩家反覆對戰後，總會被玩家掌握到弱點和破綻，導致遊戲體驗下降。AI的多樣性、多變性，成為了一個大宗的需求。這也表示了現行的AI設計，不能再以Hard Code Scripting方式進行開發。本研究提出一個將NPC行為個性化的方法。透過萃取遊戲狀態(State)的特徵，作為狀態優劣分析(Game state)的資訊。藉由使用狀態評分系統，將評分資訊提供給NPC行為決策、最佳化行為參數生成使用，並利用快速NPC行為預測系統得到預測遊戲狀態(state)資訊。採用Hierarchical Portfolio Search (HPS)的概念，將NPC行為、能力、思維模式歸納為不同的Partial Player，依據NPC能力產出合適的、更小的動作集合，採用動作評分系統對NPC動作集合評分，最後依據NPC思維模式，從眾多候選行為中選出一個最佳解，將複雜度高的遊戲，化繁為簡，實現可個性化行為NPC架構。
我們將可個性化NPC架構應用在撞球AI設計上，藉由分析撞球盤面戰略元件得到的盤面、桿法評分參數，讓撞球AI具備解球、作球能力。由實驗結果得知，在不同的參數權重的設定上，讓AI展現出截然不同的風格與能力。

When the game complexity is getting higher, the strategy game faces many challenges, and these challenges have already become issues in the field of AI research. Due to the large game state, action spaces, existing search algorithms have difficulty to make a strong decision in such a large search space. It seriously affects the development of AI. In addition, there is an increasing demand for AI. Many game AIs are grasped their weakness and flaws, after the players repeatedly played on the game, resulting in the game experience decline. AI's diversity, variability, has become a bulk demand. This also means that the current AI design, can no longer use hard-code-scripting way to develop. This study presents a method of styleable NPC behavior. We extract the characteristics of the game state, as the status evaluation of the information. By using the game state evaluation system, rating possible solutions. We use rapid estimate NPC behavior system to predict the game state information. Based on the concept of Hierarchical Portfolio Search (HPS), the NPC behavior’s ability and playing style are classified into different Partial Player, and the NPC action is used to produce the appropriate and smaller action set. The action evaluation system is used to score the action set of NPC. Finally, according to NPC strategy model, from a number of candidate behavior selected in a best solution. Make the complex game more simple.
We use this NPC architecture applied in the pool game AI design. By analyzing the pool state strategic components to get the state, strike unit evaluation parameters, makes pool AI have the ability to reposition itself. From the experimental results that, in the different parameters of the weight set, so that AI show a very different style and ability.

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