大老二是一個在亞洲盛行的多人卡牌遊戲。本研究提出一個以 rule-based 為基礎的人工智能來玩大老二。此方法以剩餘的手牌張數和控制回合為基礎來發 展出規則。當手中的牌多於四張時，會從已經分過層級的手牌中由低層級至高層 級的順序來打出。而當人工智能手中的牌剩二到四張時，會另外使用相對應的規 則。當致勝的步數小於等於三時，會有致勝策略模組提出能馬上獲勝的方針。我 們也為人工智能在沒有控制權時，設計了是否要保留牌和拆牌的規則。實驗結果 顯示我們所研發的人工智能可以把大老二玩得很好，甚至超越人類玩家，在實驗 中人工智能的勝率在 24.47%到 27.4%之間並且有使得獲勝得分最大化和在局勢不 好時使得剩餘卡牌張數最小化的機制。

Big Two is a popular multiplayer card game in Asia. This research proposes a rule-based AI to develop an AI agent for playing Big Two. The proposed method drives rules based on cards left and control position. The rules for two cards left, three cards left and four cards left are used to discard the card combination based on the number of cards remained on the agent’s hand. The rules for more than four cards left prioritize discarding the card combination in the classified cards from lower class to higher class. A winning strategy provides guidelines to get win immediately if the winning move less than or equal to 3. We also design the rules for the AI agent without control for holding cards and splitting cards. The experimental results show that our proposed AI agent could play Big Two well and outperform human players, presenting a winning rate around 24.47% to 27.4% with the capability to maximize the winning score and minimize the number of cards left when the chance of winning is low.

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