CRII: RI: A Deep Gameplay Framework for Strong Story Experience Management

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This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).Experience managers are intelligent agents that produce personalized stories that change based on decisions a player makes in a digital game. These agents create new and powerful types of interactive stories for art and entertainment, training applications, and personalized education. A central problem for experience managers is avoiding dead ends, which are situations where story structure is broken due to a choice by the player. This problem results in experiences with un-interesting stories, missed training sequences, and long spells without appropriate targeted educational content. This project will develop a novel experience management architecture to quickly navigate around dead end situations during real-time interaction with a human participant. The architecture is based on recent advances in deep reinforcement learning for general game playing. This experience management platform will enable new forms of real-time training and education applications.This work addresses a fundamental gap in existing experience managers that do not adversarially plan against sequences of player actions that lead to dead end situations. The specific research objectives are to create a deep reinforcement learning-based gameplay agent platform that (1) builds state spaces described by an action language, (2) identifies dead end states without performing exhaustive search, (3) relaxes assumptions of zero-sum and symmetric gameplay, and (4) solves narrative planning problems by compiling specialized narrative reasoning into a standard action language domain description. If successful, this research will significantly improve the speed and control of experience management agents and provide a pipeline to controlling existing and future specialized interactive narrative formalisms. These improvements will allow control of larger and more immersive narrative, training, and pedagogical environments compared to current systems.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.