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.