Abstract
Imitating video game players is considered one of the most stimulating challenges for the Game AI research community. The goal for a virtual player is not just to beat the game but to show some human-like playing style. In this work we describe a Case-Based Reasoning approach that learns to play the popular Ms. Pac-Man vs Ghosts video game from the traces of a human player. We evaluate the performance of our bot using both low level standard measures such as accuracy and recall, and high level measures such as recklessness (distance to the closest ghost, as it is mapped in our Ms. Pac-Man domain model), restlessness (changes of direction), aggressiveness (ghosts eaten), clumsiness (game steps the player is stuck) and survival (lives left). Results suggest that, although there is still a lot of room for improvement, some aspects of the human playing style are indeed captured in the cases and used by our bot.
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Acknowledgements
This work has been partially supported by the Spanish Committee of Economy and Competitiveness (TIN2014-55006-R, TIN2017-87330-R) and the UCM (Group 921330), project ComunicArte: Comunicación Efectiva a través de la Realidad Virtual y las Tecnologías Educativas, funded by Ayudas Fundación BBVA a Equipos de Investigación Científica 2017, and project NarraKit VR: Interfaces de Comunicación Narrativa para Aplicaciones de Realidad Virtual (PR41/17-21016), funded by Ayudas para la Financiación de Proyectos de Investigación Santander-UCM 2017.
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Miranda, M., Sánchez-Ruiz, A.A., Peinado, F. (2018). A CBR Approach for Imitating Human Playing Style in Ms. Pac-Man Video Game. In: Cox, M., Funk, P., Begum, S. (eds) Case-Based Reasoning Research and Development. ICCBR 2018. Lecture Notes in Computer Science(), vol 11156. Springer, Cham. https://doi.org/10.1007/978-3-030-01081-2_20
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