Simulating Player Behavior for Data-Driven Interactive Narrative Personalization

Abstract

Data-driven approaches to interactive narrative personalization show significant promise for applications in entertainment, training, and education. A common feature of data-driven interactive narrative planning methods is that an enormous amount of training data is required, which is rarely available and expensive to collect from observations of human players. An alternative approach to obtaining data is to generate synthetic data from simulated players. In this paper, we present a long short-term memory (LSTM) neural network framework for simulating players to train data-driven interactive narrative planners. By leveraging a small amount of previously collected human player interaction data, we devise a generative player simulation model. A multi-task neural network architecture is proposed to estimate player actions and experiential outcomes from a single model. Empirical results demonstrate that the bipartite LSTM network produces the better-performing player action prediction models than several baseline techniques, and the multi-task LSTM derives comparable player outcome prediction models within a shorter training time. We also find that synthetic data from the player simulation model contributes to training more effective interactive narrative planners than raw human player data alone.