G. Liapis, Lazaridis A., Vlahavas I., “Escape Room Experience for Team Building Through Gamification Using Deep Reinforcement Learning”, 15th European Conference on Game Based Learning (ECGBL), September 2021
Gamification, which is considered to be an efficient practice for learning through play, can be
significantly expanded by Artificial Intelligence methods, and particularly Machine Learning. Nowadays,
different industries employ a variety of applications based on gamification to create coherent and effective
teams, e.g., by assigning roles based on the knowledge, understanding, and relationships between members. In
this paper, we explore an online Escape Room experience that incorporates a variety of Raven-inspired
intelligence tests and team-members communication, combined with Machine Learning methods. More
particularly, we implemented state-of-the-art Deep Reinforcement Learning (Deep RL) agents, which are used
for emulating human-like behaviour to navigate and interact with the 3D rooms, as well as to solve the tests.
The RL agents simulate behavioural elements based on OCEAN personality traits model, such as openness,
conscientiousness, and neuroticism, while also generating a big number of gameplay data. Analysis shows that
their particular behavioural patterns have a significant effect on their performance, stability and time required
to solve tasks. These findings allowed us to produce new performance metrics for a generic escape room
model, which can categorize human play styles according to the OCEAN Five personality trait model. This
approach effectively analyses the teams’ behaviour concerning both individual and overall performance.