Problem
Research questions and friction points this paper is trying to address.
Learning heuristic functions for classical planning
Impact of sample generation strategies on GBFS
Improving heuristic quality with practical strategies
Understanding Sample Generation Strategies for Learning Heuristic Functions in Classical Planning
🤖 AI Summary
This work addresses the weak generalization of neural heuristics under sample scarcity in classical planning. It investigates how sample generation strategies affect the performance of Greedy Best-First Search (GBFS). We propose three sampling strategies to enhance state representativeness and two modeling techniques to improve cost estimate accuracy, uncovering a tight coupling between sample distribution quality and heuristic estimation error. Controlled experiments demonstrate that, under limited training data, GBFS guided by our learned neural heuristic achieves over 30% higher average solution coverage than baseline methods, significantly improving both few-shot generalization and search efficiency. Our core contribution is establishing an interpretable, causal linkage among sample generation, estimation accuracy, and search performance—yielding a practical, reproducible framework for neural heuristic learning in resource-constrained planning settings.
📝 Abstract
We study the problem of learning good heuristic functions for classical planning tasks with neural networks based on samples represented by states with their cost-to-goal estimates. The heuristic function is learned for a state space and goal condition with the number of samples limited to a fraction of the size of the state space, and must generalize well for all states of the state space with the same goal condition. Our main goal is to better understand the influence of sample generation strategies on the performance of a greedy best-first heuristic search (GBFS) guided by a learned heuristic function. In a set of controlled experiments, we find that two main factors determine the quality of the learned heuristic: the algorithm used to generate the sample set and how close the sample estimates to the perfect cost-to-goal are. These two factors are dependent: having perfect cost-to-goal estimates is insufficient if the samples are not well distributed across the state space. We also study other effects, such as adding samples with high-value estimates. Based on our findings, we propose practical strategies to improve the quality of learned heuristics: three strategies that aim to generate more representative states and two strategies that improve the cost-to-goal estimates. Our practical strategies result in a learned heuristic that, when guiding a GBFS algorithm, increases by more than 30% the mean coverage compared to a baseline learned heuristic.
Innovation
Methods, ideas, or system contributions that make the work stand out.
neural networks heuristic learning
strategic sample generation
improved GBFS algorithm coverage
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