Dealing with Adversarial Player Strategies in the Neural Network Game iNNk through Ensemble Learning

Applying neural network (NN) methods in games can lead to various With the recent boom in neural network (NN) applications, game new and exciting game dynamics not previously possible. However, designers have been increasingly exploring a variety of NN approaches they also lead to new challenges such as the lack of large, in computer games [65]. These include approaches where clean datasets, varying player skill levels, and changing gameplay a NN is directly incorporated into the gameplay experience or as a strategies. In this paper, we focus on the adversarial player strategy method for dynamically generating content that would otherwise aspect in the game iNNk, in which players try to communicate be created by a human artist [65]. This approach has been utilized secret code words through drawings with the goal of not being in well-known games such as Black and White [36], Creatures [23], deciphered by a NN. Some strategies exploit weaknesses in the NN and Forza Motosport [17], which adapt game agent behavior in response that consistently trick it into making incorrect classifications, leading to player input. In these cases, the NN makes gameplay to unbalanced gameplay. We present a method that combines more personalized and potentially more engaging.