Algorithmic Prompt-Augmentation for Efficient LLM-Based Heuristic Design for A* Search

2026-01-27

AI Summary

This work proposes A-CEoH, a domain-agnostic method for automatically generating high-quality heuristic functions for A* search without relying on manual expert design. By embedding the A* algorithm code directly into the prompting context and leveraging the in-context learning capabilities of large language models (LLMs) within an Evolutionary framework for Heuristics (EoH), A-CEoH autonomously constructs effective heuristics with no human intervention. Evaluated on benchmark domains such as the Uncertain Partially Observable Multi-agent Pathfinding Problem (UPMP) and sliding tile puzzles, the heuristics produced by A-CEoH significantly outperform existing baselines and even surpass handcrafted expert-designed heuristics, thereby overcoming the longstanding dependency of traditional A* search on human-crafted domain knowledge.

Technology Category

• Search and Optimization: Heuristic Search
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Planning, Routing, and Scheduling: Learning for Planning and Scheduling

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Heuristic functions are essential to the performance of tree search algorithms such as A*, where their accuracy and efficiency directly impact search outcomes. Traditionally, such heuristics are handcrafted, requiring significant expertise. Recent advances in large language models (LLMs) and evolutionary frameworks have opened the door to automating heuristic design. In this paper, we extend the Evolution of Heuristics (EoH) framework to investigate the automated generation of guiding heuristics for A* search. We introduce a novel domain-agnostic prompt augmentation strategy that includes the A* code into the prompt to leverage in-context learning, named Algorithmic - Contextual EoH (A-CEoH). To evaluate the effectiveness of A-CeoH, we study two problem domains: the Unit-Load Pre-Marshalling Problem (UPMP), a niche problem from warehouse logistics, and the classical sliding puzzle problem (SPP). Our computational experiments show that A-CEoH can significantly improve the quality of the generated heuristics and even outperform expert-designed heuristics.

Problem

• heuristic design
• A* search
• large language models
• automated heuristic generation
• prompt augmentation

Innovation

• prompt augmentation
• large language models
• heuristic design
• A* search
• in-context learning