High-Fidelity Simulated Players for Interactive Narrative Planning

High-Fidelity Simulated Players for Interactive Narrative Planning

Pengcheng Wang, Jonathan Rowe, Wookhee Min, Bradford Mott, James Lester

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
Main track. Pages 3884-3890. https://doi.org/10.24963/ijcai.2018/540

Interactive narrative planning offers significant potential for creating adaptive gameplay experiences. While data-driven techniques have been devised that utilize player interaction data to induce policies for interactive narrative planners, they require enormously large gameplay datasets. A promising approach to addressing this challenge is creating simulated players whose behaviors closely approximate those of human players. In this paper, we propose a novel approach to generating high-fidelity simulated players based on deep recurrent highway networks and deep convolutional networks. Empirical results demonstrate that the proposed models significantly outperform the prior state-of-the-art in generating high-fidelity simulated player models that accurately imitate human players’ narrative interactions. Using the high-fidelity simulated player models, we show the advantage of more exploratory reinforcement learning methods for deriving generalizable narrative adaptation policies.
Keywords:
Multidisciplinary Topics and Applications: Computer Games
Multidisciplinary Topics and Applications: Interactive Entertainment