Language Models can Infer Action Semantics for Symbolic Planners from Environment Feedback
This addresses the challenge of enabling symbolic planners to operate without expert-defined action semantics, improving automation in planning tasks.
The paper tackles the problem of automatically learning domain-specific logical action semantics for symbolic planners by combining large language models (LLMs) and symbolic planning, boosting plan success rates from 36.4% to 100% in experiments across 7 environments.
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.