HTN Learning via Transfer Learning of Domain Landmarks


  • Greg Pennisi Knexus Research Corporation
  • Morgan Fine-Morris Lehigh University
  • Michael W. Floyd Knexus Research Corporation
  • Bryan Auslander Knexus Research Corporation
  • Hector Munoz-Avila Lehigh University
  • Jeff Heflin Lehigh University
  • Kalyan Moy Gupta Knexus Research Corporation



Automated planning, Hierarchical Task Networks, transfer learning


Hierarchical Task Network (HTN) planning uses task-subtask relationships to break complex problems into more manageable subtasks, similar to how human problem-solvers plan. However, one limitation of HTN planning is that it requires domain knowledge in the form of planning methods to perform this task decomposition. Recent work has partially alleviated this knowledge engineering requirement by learning HTN methods from traces of observed behavior. Although this greatly reduces the amount of knowledge that must be encoded by a domain expert, it requires a large collection of traces in order to infer important landmark states that are used during trace segmentation and method learning. In this paper we present a novel method for landmark inference that transfers knowledge of landmarks from previously encountered environments to new environments without requiring any traces from the new environment. We evaluate our work in a logistics planning domain and show that our approach performs comparably to the existing landmark inference method but requires far fewer traces.




How to Cite

Pennisi, G., Fine-Morris, M., Floyd, M. W., Auslander, B., Munoz-Avila, H., Heflin, J., & Gupta, K. M. (2021). HTN Learning via Transfer Learning of Domain Landmarks. The International FLAIRS Conference Proceedings, 34.



Main Track Proceedings