SOURCE-LINKED INTELLIGENCE

Which LLM is Best for Translating Natural Language Goals to PDDL

arXiv · AI, language, vision and robotics · article · Sep 16, 2026 · UTC

Bridging the gap between human intent and machine execution remains a challenge in automated planning, where expressing goals in formal languages like PDDL restricts accessibility to non-experts. This paper empirically evaluates whether current Large Language Models (LLMs) can reliably translate natural language testing goals, written in informal language by video game testers, into well-formed PDDL targets suitable for classical planning. We present a carefully designed prompt template, integrating insights from iterative experimentation, aimed at maximizing both accuracy and response coheren

Read original source ↗ Open in workspace

recordType

paper

region

Global