Simulating Human Behaviour in Games using Machine Learning

The study of Artificial Intelligence and, more specifically, Machine learning in games is of great interest to the gaming industry due to their wide application in several scenarios and their capabilities of simulating human behavior by non-playable characters. In online multiplayer scenarios, one of the greatest concerns is player disconnections and how to replace this player with a "good" replacement bot. In this work, we propose a machine learning based methodology to simulate the behavior of players that learns from their gaming history that generates more balance in the game. Our work uses Multi Layer Perceptron Neural Networks evaluated in a classic card game called Hearts, in order to emulate some previously defined behaviors. The results obtained in the experiments indicate that the proposed method has very good performance, since all generated models have managed to approach similar amount of victories when comparing to the behaviors that they were trained with. Through the evaluation of the results of 10,000 matches, with gameseeds of different matches used for training, the best result was for the model Shooter versus 3 Sheriffs getting 95.5% of the amount of wins compared to their particular bot. We also conclude that behavior learning is also clear on the difference of wins in all results depending on opponent skills, i.e., opponents who were difficult to win remain difficult to win in the simulated environment.