Leveraging efficient planning and lightweight agent definition: a novel path towards emergent narrative

Abstract

Emergent narrative has the ability to unlock the true potential of interactive media, moving beyond pre-scripted, fixed story- lines. Existing implementations of emergent narrative achieve their results through complex rule systems and agent representations, which entail high authoring workload that limit the feasible scope of storyworlds. In this paper, we propose an approach that instead aims at leveraging efficient planning to achieve similar results, using Monte Carlo Tree Search and efficient data structures. This allows for abstraction and modularization of agent behavior, and endows agents with a theory of mind by letting them plan for each other. This greatly simplifies agent definition and removes the need to explicitly encode intentions. We show that competitive, collaborative and sustainable behaviors emerge in our system, without the explicit definition of such behaviors. Based on these preliminary results, we discuss necessary steps to turn our approach into an applicable emergent narrative system.