Genetic Encoding of Synergies: Coevolving Traits, Behaviors, and Strategies for Game AI - Schurer, Lucas; Silva, Luis A. L.; Pozzer, Cesar T.; Assunção, Joaquim V. C. (Anais do Simpósio Brasileiro de Jogos e Entretenimento Digital (SBGames); 2025: Anais do XXIV Simpósio Brasileiro de Jogos e Entretenimento Digital, 2025)
Introduction: Several games use data to train machine learning models, which requires extensive gameplay before creating the model. Moreover, these models are designed to be strong in the game, rather than adapting to the player’s level. Objective: To address this problem, this work focuses on coevolution through the embedded use of Genetic Algorithms (GAs) to adapt the behaviors of game characters to those of human players. Methodology: We describe how the various elements (characters’ traits, weapons, and skills) of a developed 3D game were projected and implemented, showing a concrete instance of how to develop similar games. Then, this work details the mapping of these elements to the GAs so that groups of characters represent populations, and generations represent the rounds of a match. To evaluate the coevolution of agents with the proposed adaptation method, the combined use (or synergies) between weapons, traits, behaviors, and strategies are analyzed in diverse scenarios. Results: The experiments show that characters generated with GAs adapt effectively to different types of opponents and exhibit coherent strategic behavior, confirming the potential of the coevolution method to produce dynamic and responsive game agents.