分析沃森的战略玩危险！
Analysis of Watson's Strategies for Playing Jeopardy!
摘要：
IBM Watson需要问答技术的重大进步才能玩Jeopardy！在冠军级别--节目要求对具有挑战性的自然语言问题快速回答，广泛的一般知识，高精度和准确的置信度估计。此外，Jeopardy！具有四种具有重要战略意义的决策类型：（1）每日双倍下注;（2）最后的危险下注;（3）在控制棋盘时选择下一个方格;（4）决定是否尝试回答，即，“进来吧。与简单的“经验法则”策略相比，使用复杂的策略来做出这些决定，正确地考虑游戏状态和未来事件的概率，可以显着提高玩家的整体获胜机会。本文介绍了我们的方法来开发屈臣氏的游戏策略，包括开发一个忠实的模拟模型，然后使用学习和蒙特卡洛方法在模拟器中，以优化屈臣氏的战略决策。在详细描述了我们的每一种游戏策略算法之后，我们特别关注验证模拟器预测的准确性，并记录使用我们的方法的性能改进。定量性能的好处示出相对于两个简单的启发式策略，和实际的人类参赛者在历史情节的表现。我们进一步扩展了我们对人类游戏的分析，以获得一些有价值的和违反直觉的例子，说明人类参赛者如何提高他们在节目中的表现。
英文摘要：
Major advances in Question Answering technology were needed for IBM Watson to play Jeopardy! at championship level -- the show requires rapid-fire answers to challenging natural language questions, broad general knowledge, high precision, and accurate confidence estimates. In addition, Jeopardy! features four types of decision making carrying great strategic importance: (1) Daily Double wagering; (2) Final Jeopardy wagering; (3) selecting the next square when in control of the board; (4) deciding whether to attempt to answer, i.e., "buzz in." Using sophisticated strategies for these decisions, that properly account for the game state and future event probabilities, can significantly boost a players overall chances to win, when compared with simple "rule of thumb" strategies. This article presents our approach to developing Watsons game-playing strategies, comprising development of a faithful simulation model, and then using learning and Monte-Carlo methods within the simulator to optimize Watsons strategic decision-making. After giving a detailed description of each of our game-strategy algorithms, we then focus in particular on validating the accuracy of the simulators predictions, and documenting performance improvements using our methods. Quantitative performance benefits are shown with respect to both simple heuristic strategies, and actual human contestant performance in historical episodes. We further extend our analysis of human play to derive a number of valuable and counterintuitive examples illustrating how human contestants may improve their performance on the show.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
