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Mg. Martinez-Araneda, Claudia
Research Outputs
Teach me to play, gamer! Imitative learning in computer games via linguistic description of complex phenomena and decision trees
2023, Clemente Rubio-Manzano, Lermanda, TomĂ¡s, Martinez-Araneda, Claudia, Christian Vidal & Alejandra Segura
In this article, we present a new machine learning model by imitation based on the linguistic description of complex phenomena. The idea consists of, first, capturing the behaviour of human players by creating a computational perception network based on the execution traces of the games and, second, representing it using fuzzy logic (linguistic variables and if-then rules). From this knowledge, a set of data (dataset) is automatically created to generate a learning model based on decision trees. This model will be used later to automatically control the movements of a bot. The result is an artificial agent that mimics the human player. We have implemented, tested and evaluated this technology from two different points of view: performance by using classical metrics (accuracy, ROC area and PRC area) and believability by using a Turing test for trained bots. The results obtained are interesting and promising, showing that this method can be a good alternative to design and implement the behaviour of intelligent agents in video game development.