Moving towards automated game play-testing - Malosto, Celso Gabriel; Campos, Luciana; Knop, Igor de Oliveira (Anais Estendidos do Simpósio Brasileiro de Jogos e Entretenimento Digital (SBGames); 2025: Anais Estendidos do XXIV Simpósio Brasileiro de Jogos e Entretenimento Digital, 2025)
Introduction: Prototyping games requires multiple testing phases, some of which focus on balancing rules and keeping the game fun. These are typically done by human testers, who can influence the results while requiring a significant time investment. Objective: This work in progress reports the implementation of a framework for stress testing games in the development phase. Steps: The data for decision-making is generated by intelligent agents trained in an AlphaZero-inspired method, which aligns residual neural networks with the Monte-Carlo Tree Search algorithm. Expected results: Game designers should be able to describe their game on our platform. The process of training and using agents will allow us to capture eventual balancing problems and dominant strategies, providing valuable information to guide changes in the rules, thus improving the player experience.