Geeks and guests: Estimating player's level of experience from board game behaviors

Abstract

Board games have become promising tools for observing and studying social behaviors in multi-person settings. While traditional methods such as self-report questionnaires are used to analyze game-induced behaviors, there is a growing need to automate such analyses. In this paper, we focus on estimating the levels of board game experience by analyzing a player's confidence and anxiety from visual cues. We use a board game setting to induce relevant interactions, and investigate facial expressions during critical game events. For our analysis, we annotated the critical game events in a multiplayer cooperative board game, using the publicly available MUMBAI board game corpus. Using off-the-shelf tools, we encoded facial behavior in dyadic interactions and built classifiers to predict each player's level of experience. Our results show that considering the experience level of both parties involved in the interaction simultaneously improves the prediction results.