Neural Story Planning

Ye, Anbang; Christopher, Cui,; Shi, Taiwei; Riedl, Mark O.

doi:10.48550/arxiv.2212.08718

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2024

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“…One interesting method of achieving global story coherence was proposed in Neural Story Planning [24]. It mimics a partial order planning algorithm [25] that works backwards from the end of the story.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Planning Stories Neurally

Farrell,

Ware

2024

Preprint

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Symbolic planning algorithms and large language models have different strengths and weaknesses for story generation, suggesting hybrid models might leverage advantages from both. Others have proposed using a language model in combination with a partial order planning style algorithm to avoid the need for a handwritten symbolic domain of actions. This paper offers a complementary approach. We use a state space planning algorithm to plan coherent multi-agent stories in symbolic domains, with a language model acting as a guide to estimate which events are worth exploring first. We evaluate an initial implementation of this method on a set of benchmark problems and find that the LLM’s guidance is helpful to the planner in most domains.

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Section: Introduction and Related Workmentioning

confidence: 99%