摘要
合約橋牌是一種具有不完全資訊特性的遊戲，電腦在此遊戲中通常無法勝過人類的橋牌專家。其中，人類橋牌玩家的叫牌決定對於電腦程式而言特別難以模仿，這使得自動化叫牌仍然是一個具挑戰性的研究問題。另一方面，使用不模仿人類玩家的方法進行自動化叫牌的可能性目前尚未被充份研究，在這篇論文中，我們在無競叫叫牌問題上首先探討使用此種方法的可能性。我們提出一個獨創的機器學習架構以使電腦程式學習自己的叫牌決定。在這個架構下，我們將叫牌問題轉換為機器學習問題，並精心設計一個基於成本導向分類器和信心值上界演算法的模型以解決此問題。我們以實驗驗證所提出的模型，並發現此模型與模仿人類玩家叫牌決定且多次贏得冠軍的電腦橋牌程式相較具有相當的競爭力。
並列摘要
Contract bridge is an example of an incomplete information game for which computers typically do not perform better than expert human bridge players. In particular, the typical bidding decisions of human bridge players are difficult to mimic with a computer program, and thus automatic bridge bidding remains to be a challenging research problem. Currently, the possibility of automatic bidding without mimicking human players has not been fully studied. In this work, we take an initiative to study such possibility for the specific problem of bidding without competition. We propose a novel learning framework to let a computer program learn its own bidding decisions. The framework transforms the bidding problem into a learning problem, and then solves the problem with a carefully designed model that consists of costsensitive classifiers and upper-confidence-bound algorithms. We validate the proposed model and find that it performs competitively to the champion computer bridge program that mimics human bidding decisions.
參考文獻
[1] Tuomas Sandholm. The state of solving large incomplete-information games, and
learning and games. Machine learning, 63(3):211–215, 2006.
Bayes-relational learning of opponent models from incomplete information in nolimit
poker. In Proceedings of the AAAI Conference on Artificial Intelligence, pages
1485–1486, 2008.