Decompositional planning combines hierarchical reasoning as discussed in hierarchical task networks (HTNs) and least-commitment refinement reasoning as discussed in partial-ordercausal link planning (POCL) in a unified knowledge representation and a sound and complete reasoning procedure. This project involves developing computational machinery designed to expand the applicability of decompositional planning.

Publications

• David R. Winer and Rogelio E. Cardona-Rivera; A Depth-Balanced Approach to Decompositional Planning for Problems where Hierarchical Depth is Requested. In Proceedings of the 1st Workshop on Hierarchical Planning at the 28th International Conference on Automated Planning and Scheduling (ICAPS 2018), pages 1-8, Delft, The Netherlands, 2018.

• Rogelio E. Cardona-Rivera and R. Michael Young; Toward Combining Domain Theory and Recipes in Plan Recognition. In Proceedings of the Plan, Activity, and Intent Recognition Workshop at the 31st AAAI Conference on Artificial Intelligence (PAIR 2017), pages 796-803, San Francisco, CA, USA, 2017.
  (40% acceptance rate)

Project Details

Name: Decompositional Planning

Project Stage: Harvest

Launched: 2017

Categories: Artificial Intelligence

Tags: Decompositional Planning,Least-commitment Planning

Sponsor:

Department of Energy Computational Science Graduate Fellowship

External Site: None.