Neuro-Symbolic Procedural Planning with Commonsense Prompting

Yujie Lu, Weixi Feng, Wanrong Zhu, Wenda Xu, Xin Eric Wang, Miguel Eckstein, William Yang Wang

Keywords: Procedural Planning, Commonsense Knowledge, Prompting, Neuro-Symbolic

TL;DR: We propose a neuro-symbolic procedural planner that elicits procedural planning knowledge from the large language models with commonsense-infused prompting. We achieve state-of-the-art performance on WikiHow and RobotHow.

Abstract: Procedural planning aims to implement complex high-level goals by decomposition into simpler low-level steps. Although procedural planning is a basic skill set for humans in daily life, it remains a challenge for large language models (LLMs) that lack a deep understanding of the cause-effect relations in procedures. Previous methods require manual exemplars to acquire procedural planning knowledge from LLMs in the zero-shot setting. However, such elicited pre-trained knowledge in LLMs induces spurious correlations between goals and steps, which impair the model generalization to unseen tasks. In contrast, this paper proposes a neuro-symbolic procedural PLANner (PLAN) that elicits procedural planning knowledge from the LLMs with commonsense-infused prompting. To mitigate spurious goal-step correlations, we use symbolic program executors on the latent procedural representations to formalize prompts from commonsense knowledge bases as a causal intervention toward the Structural Causal Model. Both automatic and human evaluations on WikiHow and RobotHow show the superiority of PLAN on procedural planning without further training or manual exemplars.

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