Höller, Daniel; Behnke, Gregor
Loop Detection in the PANDA Planning System
Proceedings of the 31st International Conference on Automated Planning and Scheduling (ICAPS), 31, AAAI Press, pp. 168-173, 2021.
The International Planning Competition (IPC) in 2020 wasthe first one for a long time to host tracks on Hierarchical
Task Network (HTN) planning. HYPERTENSION, the winner of the tack on totally-ordered problems, comes with an interesting technique: it stores parts of the decomposition path in the state to mark expanded tasks and forces its depth first
search to leave recursive structures in the hierarchy. This can be seen as a form of loop detection (LD) – a technique that is
not very common in HTN planning. This might be due to the spirit of encoding enough advice in the model to find plans
(so that loop detection is simply not necessary), or because it becomes a computationally hard task in the general (i.e.
partially-ordered) setting. We integrated several approximate and exact techniques for LD into the progression search of the
HTN planner PANDA. We test our techniques on the benchmark set of the IPC 2020. Both in the partial ordered and total
ordered track, PANDA with LD performs better than the respective winner of the competition.