LLM-Generated Heuristics for AI Planning: Do We Even Need Domain-Independence Anymore?
This work addresses the challenge of domain independence in AI planning for researchers and practitioners, offering a potentially incremental shift by complementing traditional methods.
The paper tackles the problem of AI planning by using large language models (LLMs) to generate domain-specific heuristics from task descriptions, achieving state-of-the-art performance on some standard IPC domains and solving problems without PDDL representations.
Domain-independent heuristics have long been a cornerstone of AI planning, offering general solutions applicable across a wide range of tasks without requiring domain-specific engineering. However, the advent of large language models (LLMs) presents an opportunity to generate heuristics tailored to specific planning problems, potentially challenging the necessity of domain independence as a strict design principle. In this paper, we explore the use of LLMs to automatically derive planning heuristics from task descriptions represented as successor generators and goal tests written in general purpose programming language. We investigate the trade-offs between domain-specific LLM-generated heuristics and traditional domain-independent methods in terms of computational efficiency and explainability. Our experiments demonstrate that LLMs can create heuristics that achieve state-of-the-art performance on some standard IPC domains, as well as their ability to solve problems that lack an adequate Planning Domain Definition Language ({\sc pddl}) representation. We discuss whether these results signify a paradigm shift and how they can complement existing approaches.