论文标题
使用词来学习设计和比较策略
Using Wordle for Learning to Design and Compare Strategies
论文作者
论文摘要
Wordle是《纽约时报》拥有的非常受欢迎的单词游戏。我们可以根据有关游戏的概率，统计和信息理论信息设计参数化的策略，以求解文字。这些策略可以系统地和动态地处理相当大的类似Wordle的游戏家族，这意味着我们不依赖可能适用于非固定游戏的预先计算。更具体地说，答案集可以是任意的，而不是局限于当前的2315个单词。答案单词可能包括任何特定数量的字母（不必是五个），并且形成单词的符号集不必仅限于英语字母。 探索解决类似文字的游戏的可能策略为学习设计计算机游戏的学生提供了一个有吸引力的学习挑战。本文将提供使用两个参数化策略系列来解决当前文字的结果，该策略使用遵守硬模式规则作为基线的模拟器。基线模拟器平均使用4.078猜测来找到2315个答案，并且需要六次以上的试验来解决游戏的时间1.77％。我们的表现最好的策略平均使用3.674猜测来找到2315个答案，并且失败了0.65％的时间。
Wordle is a very popular word game that is owned by the New York Times. We can design parameterized strategies for solving Wordle, based on probabilistic, statistical, and information-theoretical information about the games. The strategies can handle a reasonably large family of Wordle-like games both systematically and dynamically, meaning that we do not rely on precomputations that may work for non-fixed games. More specifically, the answer set can be arbitrary, not confining to the current 2315 words. The answer words may include any specific number of letters (does not have to be five), and the set of symbols that form the words does not have to be limited to only the English alphabet. Exploring possible strategies for solving the Wordle-like games offers an attractive learning challenges for students who are learning to design computer games. This paper will provide the results of using two families of parameterized strategies to solve the current Wordle, using the simulator that abides by the hard-mode rules as the baseline. The baseline simulator used an average of 4.078 guesses to find the 2315 answers, and needed more than six trials to solve the game 1.77% of the time. The best performing strategy of ours used an average of 3.674 guesses to find the 2315 answers, and failed 0.65% of the time.
