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.
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This paper is the result of work by the SOMOS research group (SOftware-MOdelling-Science), funded by the Universidad del Bío-Bío, Chile. The authors thank the Facultad de Ingeniería de la Universidad Católica de la Santísima Concepción, Chile.
The authors have not disclosed any funding. Partially supported by the research project DIUBB 170915 2/R by the University of the Bío-Bío.
Conflict of interest
Clemente Rubio, Tomás Lermanda, Claudia Martínez, Christian Vidal and Alejandra Segura declare that they have no conflict of interest.
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Rubio-Manzano, C., Lermanda, T., Martínez-Araneda, C. et al. Teach me to play, gamer! Imitative learning in computer games via linguistic description of complex phenomena and decision trees. Soft Comput 27, 3023–3035 (2023). https://doi.org/10.1007/s00500-022-07476-z
- Imitative learning
- Machine learning
- Linguistic description of complex phenomena
- Computer games
- Intelligence agents