NEURO-SYMBOLIC PROCEDURAL PLANNING WITH COMMONSENSE PROMPTING

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 knowledge from LLMs in the zero-shot setting. However, such elicited pre-trained knowledge in LLMs induces spurious correlations between goals and steps, impairing the model's generalization to unseen tasks. In contrast, this paper proposes a neuro-symbolic procedural PLANner (PLAN) that elicits procedural 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 external knowledge bases as a causal intervention toward the Structural Causal Model of procedural planning. Both automatic and human evaluations on WikiHow and RobotHow show the superiority of PLAN on procedural planning without further training or manual exemplars. 1 

1. INTRODUCTION

How to make a cup of coffee? As humans, we can easily specify a procedure to solve this task, using our innate ability of commonsense reasoning. However, can we endow machines with the same ability to construct a sequential plan? As depicted in Figure 1 , procedural planning (Pearson, 1996; Zhang et al., 2020b; Huang et al., 2022) aims to decompose a high-level goal (Task: Watch TV) into a sequence of temporally extended steps (Procedural Plan: Step at all five time-steps). We study procedural planning as the conditional text generation problem since it resembles real-world scenarios. Previous approaches (Huang et al., 2022; Ahn et al., 2022) require a small number of carefully written or held-out exemplars to acquire procedural knowledge. However, these manual exemplars evolved from task data are impossible to cover the ever-changing task setups and the flexible dependency relations among goals and steps. In fact, the biased data may cause the model to learn spurious correlations and hinder the model from generalizing well in zero-shot scenarios. Studies in cognitive science show that humans rely on chunking mechanisms (Gobet et al., 2001; Miller, 1956) which turn primitive stimuli into conceptual groups to solve novel and complex problems. Inspired by this, we hypothesize that generalizable procedural planning ability can be achieved by learning cause-effect relations among complex goals and simpler steps using external knowledge. To reveal the cause-effect relations in procedural planning, we devise a Structural Causal Model (SCM) (Peters et al., 2017) , a directed acyclic graph commonly used to describe the causal relationships within a system Pearl (2009). As depicted in Figure 2 , the pre-trained knowledge (D) (e.g., TV and living room is highly correlated) in LLMs confounds (D influences T , S i-1 and S i , resulting in spurious correlations) the system to make biased decisions toward an unreasonable step (e.g., Find



Source code and datasets are publicly available at https://sites.google.com/view/iclr-clap 1

