LLM-Evolved Domain-Independent Heuristics for Symbolic AI Planning
Heuristic search is the dominant paradigm in symbolic AI planning, and the strongest heuristics are the result of decades of work by planning researchers. Recent work has shown that large language models (LLMs) can design heuristics for individual planning domains, but no LLM-generated heuristic has so far worked on arbitrary planning tasks. In this paper, we use evolutionary search to produce the first LLM-generated domain-independent heuristics that exceed the hand-engineered state of the art. We let an LLM mutate parent heuristics written in C++, store candidates in a MAP-Elites archive keyed on informedness and speed and calculate fitness scores by blending coverage with solving time....
| Authors | Elliot Gestrin, Jendrik Seipp |
|---|---|
| Primary category | cs.AI |
| Published | 28 May 2026 |
| Last updated at source | 17 June 2026 |
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