Loop Detection in the PANDA Planning System

Abstract

The International Planning Competition (IPC) in 2020 was the 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.

The 2023 International Planning Competition

“…Lifted planning did prove to remain worthwhile especially in the TO agile track: Four domains were solved best by SIADEX alone and three domains were solved best by LTP alone. All in all, the results, however, emphasize the progress brought by the latest progression search approaches with efficient and effective grounding (Behnke et al 2020), new pruning strategies (Höller and Behnke 2021), and informed search decisions (Höller et al 2019). These approaches outperform lifted and translation-based approaches on the majority of domains but do not fully dominate them.…”

“…All in all, the results, however, emphasize the progress brought by the latest progression search approaches with efficient and effective grounding (Behnke et al. 2020), new pruning strategies (Höller and Behnke 2021), and informed search decisions (Höller et al. 2019).…”

Flexible FOND HTN Planning: A Complexity Analysis

“…A safe but loose upper bound for f is the exponential function with a brute force algorithm for checking task network isomorphism. In practice, we can apply GI algorithms in literature with upper bound O(e √ n log n ) Babai, Kantor, and Luks 1983) or recent task network isomorphism solvers (Höller and Behnke 2021). Furthermore, almost all graphs are easy to solve as seen in the nauty package (McKay and Piperno 2014).…”

A Look-Ahead Technique for Search-Based HTN Planning: Reducing the Branching Factor by Identifying Inevitable Task Refinements

“…Hierarchical Task Network (HTN) planning aims to find an executable sequence of actions that is a refinement of some initial abstract tasks (Erol, Hendler, and Nau 1996;Ghallab, Nau, and Traverso 2004;Alford, Bercher, and Aha 2015). HTN planning problems can be solved in various ways (Bercher, Alford, and Höller 2019), but one of the most successful ones at the moment is phrasing it -just like in classical planning -as a heuristic search problem (Höller and Behnke 2021). In a progression search-based HTN planner search nodes consist of a current state and a task network, which is a collection of primitive and/or abstract tasks (Nau et al 2003;Höller et al 2020b).…”

“…We implemented the look-ahead technique as well as the generalized progression procedure on top of the PANDA progression planner (Höller and Behnke 2021;Höller et al 2020b). We conducted a comprehensive empirical evaluation on the benchmark set of the International Planning Competition (IPC) 2020, which shows that our proposed technique outperforms the state of the art in terms of solved instances and IPC score.…”