Deterministic Algorithmic Approaches to Solve Generalised Wordle
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Wordle is a single-player word-based game where the objective is to guess the 5-letter word in a maximum of 6 tries. The game was released to the public in October 2021 and has since gained popularity with people competing against each other to maintain daily streaks and guess the word in a minimum number of tries. There have been works using probabilistic and reinforcement learning based approaches to solve the game. Our work aims to formulate and analyze deterministic algorithms that can solve the game and minimize the number of turns required to guess the word and do so for any generalized setting of the game. As a simplifying assumption, for our analysis of all the algorithms we present, we assume that all letters will be unique in any word which is part of our vocabulary. We propose two algorithms to play Wordle - one a greedy based approach, and other based on Cliques. The Greedy approach is applicable for both hard and easy modes of Wordle, while the Clique formation based approach only works on the Easy mode. We present our analysis on both approaches one by one, next.
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