Semantic Partial Grounding via LLMs
This addresses a domain-specific problem for AI planning researchers by improving efficiency in hard-to-ground tasks, though it is incremental as it builds on existing partial grounding methods.
The paper tackles the computational bottleneck of grounding in classical planning by using LLMs to heuristically identify irrelevant elements in PDDL descriptions before grounding, resulting in faster grounding by orders of magnitude across seven benchmarks while maintaining plan quality.
Grounding is a critical step in classical planning, yet it often becomes a computational bottleneck due to the exponential growth in grounded actions and atoms as task size increases. Recent advances in partial grounding have addressed this challenge by incrementally grounding only the most promising operators, guided by predictive models. However, these approaches primarily rely on relational features or learned embeddings and do not leverage the textual and structural cues present in PDDL descriptions. We propose SPG-LLM, which uses LLMs to analyze the domain and problem files to heuristically identify potentially irrelevant objects, actions, and predicates prior to grounding, significantly reducing the size of the grounded task. Across seven hard-to-ground benchmarks, SPG-LLM achieves faster grounding-often by orders of magnitude-while delivering comparable or better plan costs in some domains.