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.

本文介绍了用概率、统计和信息理论的方法，设计参数化策略来处理 Wordle 等电脑游戏，并探索两个家族参数化策略用于解决 Wordle，并与基准解决方案进行比较得到的结果。