Modeling and Understanding Future Action Decisions of Players during Online Gaming

Abstract

Contemporary Supervised Machine Learning (SML) and explainable AI (artificial intelligence) methods can be employed to both model and understand the decision making behavior of human actors within a multi-agent task setting. Here, we apply such modeling approach to capture the decision-making behavior of human actors playing a 3-player online herding game called “Desert Herding”. Of particular interest is whether the modeling approach can be employed to predict and understand the target switching strategies of human herders at variable prediction horizons and whether the explainable AI tool SHAP can be leveraged to identify the key informational variables (features) underlying the players’ target selection decisions.