Usando LLMs para Programar Jogos de Tabuleiro e Variações

Abstract

Creating programs to represent board games can be a time-consuming task. Large Language Models (LLMs) arise as appealing tools to expedite this process, given their capacity to efficiently generate code from simple contextual information. In this work, we propose a method to test how capable three LLMs (Claude, DeepSeek and ChatGPT) are at creating code for board games, as well as new variants of existing games.

Accepted for presentation at the I Escola Regional de Aprendizado de Máquina e Inteligência Artificial da Região Sul, 2025, in Portuguese language