Prediction as faster perception in a real-time fighting video game

In a real-time video game, AI-controlled players, called agents, are still inferior to skilled human players on equal footing. In this work, we aim to construct a strong agent enough to fight with skilled human players in a real-time fighting video game. First we investigate the relation between perception speed and performance. From a simulation using two agents one of which has delayed perception, we know that perception speed is a critical factor in performance. Moreover, it means that it is effective to predict the opponent´s behavior to enhance the agent. Therefore, we construct an agent that predicts its opponent´s position and action in a fighting video game. The agent uses linear extrapolation to predict the position and the k-nearest neighbor method to predict the action. Comparing agents with and without the prediction ability, we see that the predicting agent mostly obtained higher scores than the non-predicting one in fighting with six contestants of a previous competition of the game.