arXiv:2602.22067v2 Announce Type: replace Abstract: 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
The increasing scale and capability of LLMs are enabling their application to long-standing AI challenges like planning, which previously relied on more traditional symbolic or statistical methods.
This development indicates a continued expansion of LLM utility into core AI reasoning tasks, potentially accelerating the development of more adaptive and capable autonomous systems.
LLMs can now be used to address computational bottlenecks in classical AI planning by leveraging textual and structural cues, moving beyond purely relational features or learned embeddings.
- · AI researchers
- · Developers of autonomous systems
- · Logistics and automation sectors
- · Traditional symbolic AI planning methods
- · Systems heavily reliant on computationally expensive full grounding
More efficient and scalable AI planning systems for complex environments.
Accelerated development of sophisticated AI agents capable of planning in real-world scenarios.
Enhanced automation and operational efficiency across industries as AI planning becomes more robust.
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Read at arXiv cs.AI