On Sample-Efficient Generalized Planning via Learned Transition Models

1. 1Learn a transition model from data to improve generalization across planning problems
2. 2Use Transformer-based planners, such as PlanGPT and Plansformer, to leverage learned transition models
3. 3Apply symbolic abstractions and explicit reasoning over the transition function to further improve planning efficiency
4. 4Evaluate the sample efficiency of the learned transition model and refine it as needed

AI researchers and engineers on a team benefit from this approach as it enables more efficient planning across multiple problems, and software engineers can apply these findings to develop more effective planning systems

💡 Learned transition models can improve the sample efficiency of generalized planning, enabling more effective solution strategies across multiple planning problems

💡 Learned transition models boost sample-efficient generalized planning!