LLMs are bad at planning (or long-horizon reasoning) [9] but they are good at describing and trans-
lating textual inputs, including re-writing planning prompts in the PDDL format. The intuition is
that we can view PDDL as a different language than English, so re-writing planning prompts in
PDDL is essentially a “machine translation” task that LLMs are known to excel at. Here we pro-
vide an example of a PDDL problem ﬁle written by GPT-4 without any prompt-engineering. As we
can see, the generated ﬁle appears to have the correct PDDL syntax, but uses a made-up predicate
(empty) and misses the initial condition that b1 is on the table.

An Example PDDL Problem File Written by GPT-4

Prompt: Description of Problem (P1) + Provide me with the problem PDDL ﬁle that
describes the planning problem directly without further explanations.

GPT-4 (the generated problem PDDL):
(:objects b1 b2 b3 b4 b5 - block)

(:init (on b5 b3) (on b4 b2) (on b2 b1) (on b3 b4) (clear b5)
LLM+P- Empowering Large Language Models with Optimal Planning Proficiency