arXiv:2501.18784v5 Announce Type: replace Abstract: Heuristics are a central component of deterministic planning, particularly in domain-independent settings where general applicability is prioritized over task-specific tuning. This work revisits that paradigm in light of recent advances in large language models (LLMs), which enable the automatic synthesis of heuristics directly from problem definitions -- bypassing the need for handcrafted domain knowledge. We present a method that employs LLMs to generate problem-specific heuristic functions from planning tasks specified through successor ge
Advances in large language models have reached a point where their generative capabilities can be applied to complex problem-solving domains like planning, bypassing traditional handcrafted approaches.
This development indicates a significant step towards more autonomous and adaptable AI systems, redefining how planning and decision-making heuristics are generated and applied in various operational contexts.
The reliance on manually engineered domain knowledge for heuristic generation in planning is reduced, enabling more rapid and automated deployment of AI in complex, novel environments.
- · AI developers
- · Robotics
- · Logistics and supply chain
- · Autonomous systems
- · Traditional AI planning experts (short-term)
- · Manual feature engineering techniques
AI agents will exhibit improved problem-solving capabilities without extensive human domain expertise.
The cost and complexity of deploying AI in new, unstructured environments will decrease, accelerating automation across industries.
This could lead to a proliferation of highly specialized, context-aware AI agents profoundly impacting white-collar workflows and operational efficiency.
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Read at arXiv cs.AI