Language Models can Infer Action Semantics for Symbolic Planners from Environment Feedback

TL;DR

This paper introduces PSALM, a method that combines symbolic planning and large language models to automatically learn and infer action semantics, significantly improving plan success rates and environment exploration efficiency.

Contribution

PSALM is a novel approach that iteratively learns domain-specific action semantics by leveraging LLMs and symbolic planners, enhancing planning success and efficiency.

Findings

PSALM achieves 100% plan success rate from 36.4% with Claude-3.5.

PSALM explores environments more efficiently than prior methods.

Learning from one goal, PSALM effectively infers ground truth action semantics.

Abstract

Symbolic planners can discover a sequence of actions from initial to goal states given expert-defined, domain-specific logical action semantics. Large Language Models (LLMs) can directly generate such sequences, but limitations in reasoning and state-tracking often result in plans that are insufficient or unexecutable. We propose Predicting Semantics of Actions with Language Models (PSALM), which automatically learns action semantics by leveraging the strengths of both symbolic planners and LLMs. PSALM repeatedly proposes and executes plans, using the LLM to partially generate plans and to infer domain-specific action semantics based on execution outcomes. PSALM maintains a belief over possible action semantics that is iteratively updated until a goal state is reached. Experiments on 7 environments show that when learning just from one goal, PSALM boosts plan success rate from 36.4% (on Claude-3.5) to 100%, and explores the environment more efficiently than prior work to infer ground truth domain action semantics.