LLM-evolved pattern generators yield admissible heuristics for optimal planning

Researchers have developed a novel method for learning domain-dependent heuristics that guarantee admissibility for optimal classical planning. This approach utilizes an LLM-driven evolutionary framework to synthesize programs that generate pattern collections, which are then combined using saturated cost partitioning. The resulting heuristics offer interpretable insights, run with minimal overhead, and match the performance of state-of-the-art methods while evaluating states significantly faster. AI

IMPACT Introduces a novel approach to optimal classical planning using LLM-evolved heuristics, potentially improving efficiency and interpretability in AI planning systems.

RANK_REASON This is a research paper detailing a new method for classical planning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• LLM

• paper
• other

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →