Large Language Models as Narrative Planning Search Guides
Rachelyn Farrell, Stephen G. Ware · IEEE Transactions on Games · 2024
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 hand-written symbolic domain of actions, or generating these domains from natural language input. This article offers a complementary approach. We propose to use a state space planning algorithm to plan coherent multiagent stories using hand-written symbolic domains, but with a language model acting as a guide to estimate, which events are worth exploring first. We present an initial evaluation of this approach on a set of benchmark narrative planning problems.