Semantic Partial Grounding via LLMs

2026-02-25 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, quick

Summary

SPG-LLM is a novel approach that utilizes Large Language Models (LLMs) to enhance classical planning by addressing the computational bottleneck of grounding. Traditional grounding methods face exponential growth in grounded actions and atoms with increasing task size. While recent partial grounding techniques use predictive models and relational features, SPG-LLM uniquely leverages the textual and structural information within PDDL (Planning Domain Definition Language) descriptions. It employs LLMs to heuristically identify and filter out irrelevant objects, actions, and predicates before the grounding process begins, thereby substantially reducing the size of the grounded task. Evaluated across seven challenging benchmarks, SPG-LLM demonstrated grounding speeds that were often orders of magnitude faster, alongside comparable or improved plan costs in several domains.

Key takeaway

For research scientists developing classical planning systems, SPG-LLM offers a compelling method to overcome grounding bottlenecks. You should consider integrating LLM-driven semantic pre-filtering into your planning workflows, especially for large or complex PDDL domains, to achieve substantial speedups in grounding without sacrificing plan quality. This approach could significantly improve the scalability of your planning solutions.

Key insights

LLMs can significantly accelerate classical planning by semantically pre-filtering irrelevant PDDL elements before grounding.

Principles

• Textual cues improve planning efficiency.
• Pre-filtering reduces computational load.

Method

SPG-LLM uses LLMs to analyze PDDL domain and problem files, identifying and removing irrelevant objects, actions, and predicates heuristically prior to the main grounding phase.

In practice

• Apply LLMs for PDDL analysis.
• Integrate semantic filtering into planners.

Topics

• Classical Planning
• Partial Grounding
• Large Language Models
• PDDL
• Planning Efficiency

Best for: Research Scientist, AI Researcher, AI Scientist, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.