Game Theory and Deep Learning Approach to Implement Scrabble

Integrating deep learning into the Scrabble board game introduces a level of intricacy and sophistication to the overall gaming experience. This study delves into the application of deep learning methodologies in developing a Scrabble game. Traditionally, Scrabble games have relied on rule-based systems or rudimentary algorithms. However, this research takes a groundbreaking approach by integrating deep learning to elevate the game's intelligence and adaptability. The central focus revolves around employing neural network architectures to discern and comprehend patterns within Scrabble board layouts and word placements. The study explores the potential of utilizing a convolutional neural network, specifically the U-Net architecture, to scrutinize the game board. This involves the model learning intricate relationships between letter distributions, vacant spaces, and valid word formations. The deep learning model undergoes training using a diverse dataset that encompasses various Scrabble board states, each annotated with corresponding valid letter distributions. The goal is to empower the model to predict optimal letter placements on the board while considering the inherent constraints and rules of the Scrabble game.