Using Wordle for Learning to Design and Compare Strategies

TL;DR

This paper develops and evaluates parameterized strategies for solving Wordle and Wordle-like games, demonstrating improved performance over baseline strategies in terms of guesses needed and failure rate.

Contribution

It introduces flexible, probabilistic strategies that can handle diverse Wordle variants without precomputations, advancing game-solving methods.

Findings

Best strategy reduces average guesses from 4.078 to 3.674.

Failure rate decreases from 1.77% to 0.65%.

Strategies are adaptable to various game configurations.

Abstract

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.