A binary classification approach for automatic preference modeling of virtual agents in Civilization IV

Abstract

Player Modeling tries to model players behaviors and characteristics during a game. When these are related to more abstract preferences, the process is normally called Preference Modeling. In this paper we infer Civilization IV's virtual agents preferences with classifiers based on support vector machines. Our vectors contain score indicators from agents gameplay, allowing us to predict preferences based on the indirect observations of actions. We model this task as a binary classification problem, allowing us to make more precise inference. In this sense, we leveraged previous approaches that also used kernel machines but relied on different preference levels. Using binary classification and parameter optimization, our method is able to predict some agents preferences with an accuracy of 100%. Moreover, it is also capable of generalizing to different agents, being able to predict preferences of agents that were not used in the training process.