Dealing with Adversarial Player Strategies in the Neural Network Game iNNk through Ensemble Learning
This addresses the problem of unbalanced gameplay in NN-based games for developers, though it is incremental as it adapts existing techniques to a specific domain.
The paper tackled adversarial player strategies in the neural network game iNNk, where players exploit weaknesses to trick the NN, and the result was a method combining transfer learning and ensemble learning that significantly outperformed the baseline across all strategies with limited adversarial training data.
Applying neural network (NN) methods in games can lead to various new and exciting game dynamics not previously possible. However, they also lead to new challenges such as the lack of large, clean datasets, varying player skill levels, and changing gameplay strategies. In this paper, we focus on the adversarial player strategy aspect in the game iNNk, in which players try to communicate secret code words through drawings with the goal of not being deciphered by a NN. Some strategies exploit weaknesses in the NN that consistently trick it into making incorrect classifications, leading to unbalanced gameplay. We present a method that combines transfer learning and ensemble methods to obtain a data-efficient adaptation to these strategies. This combination significantly outperforms the baseline NN across all adversarial player strategies despite only being trained on a limited set of adversarial examples. We expect the methods developed in this paper to be useful for the rapidly growing field of NN-based games, which will require new approaches to deal with unforeseen player creativity.