賽局是指兩人以上的競賽，交通路線選擇、益智遊戲的出招方式、股市投資、人際關係、公司經營、商場或政局中激烈的競爭、甚至國際情勢間的戰與和，都屬於賽局的一種，為了求得勝利而產生了各種策略、智謀，也就是賽局理論。
　　撲克屬於不完全資訊賽局的一種，由於有機率因素而使複雜度大幅提高，在所有撲克變形中，德州撲克又是複雜度最高的類型之一，若視牌桌為一個小型的經濟社會，撲克投注策略即為簡化過的投資策略，對於不完全資訊賽局是良好的研究目標。
　　本篇研究以德州撲克的下注策略為主題，進行不完全資訊賽局之研究，從雙人德州撲克賽局開始著手，以多人德州撲克之應用為目標。首先，在每次輪詢下注時，利用賽局理論中的策略矩陣求最佳策略分佈，並使用取樣及近似方法以加速計算複雜的勝率，以便利用於策略矩陣中；再將單次輪詢之策略矩陣以樹狀結構組織，形成矩陣迭代樹的模型，以簡化運算複雜度，並可在合理時間內得到德州撲克賽局之非確定性策略；最後嘗試將此結構擴張為多人賽局模型，以驗證此模型之通用性。
實驗結果顯示，使用矩陣迭代樹模型確實能得出快速而符合理論的最佳解，隨著賽局局數的增加，獲利逐漸累積上升，且同樣的模型可以輕易轉換至多人賽局中使用，也能在相差不多的短時間內得到最佳解，對於不完全資訊賽局分析而言，矩陣迭代樹的確是一個具擴張性的良好模型。

A game is a competition among people. It includes choosing traffic routes, deciding strategies in a casino, stock market investment, interpersonal relationship skills, company managing, market competitions and political strife. Even war and peace in the international situation are a kind of games. In order to win the games, a variety of ideas and strategies are proposed, which are called Game Theory.
Poker is one of the imperfect information games. Its complexity rises substantially because of the probability factor. Among all poker games, Texas Hold’em is one of the poker games with the highest complexity. If we treat the table as a little economical society, poker-betting strategy is just a simplified investment strategy, so it is a good target for studying the imperfect information games.
This thesis focuses on the study of imperfect information games with the topic of Texas Hold’em betting strategy. We start from two-players Texas Hold’em Poker and aim to k-players Texas Hold’em Poker. First, we use the strategy matrix mentioned in Game Theory to derive the optimal strategy distribution in each betting round and speed up the calculation of winning probability, which will be used in the strategy matrix, with sampling and approximation. Then we organize the matrix into a tree, form an iterated matrix tree model to simplify the computation, and get a mixed strategy of Texas Hold’em in reasonable time. Finally, we extend this structure to form a k-players game model.
The experiment results show that we can certainly get a theoretically optimal solution with iterated matrix tree model in a short time. Since more games are played, the profit has been increasing cumulatively. In addition, the same model can be easily converted to k-players mode and will also generate an optimal solution in almost the same moment. Iterated matrix tree is indeed an appropriate extendable model for analyzing imperfect information games.

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