Symbolic Search for Optimal Total-Order HTN Planning
Authors: Gregor Behnke, David Speck11744-11754
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | An empirical analysis shows that our symbolic approach outperforms the current state of the art for optimal totally-ordered HTN planning. We conclude with an empirical evaluation. Tab. 1 shows the overall coverage of all five planners on the benchmark instances. Figure 4 shows the coverage over time. Figure 5 shows a per-instance runtime comparison between the two best planners overall, auto Sym and SAT(bin). |
| Researcher Affiliation | Academia | Gregor Behnke and David Speck University of Freiburg Freiburg im Breisgau, Germany behnkeg, speckd @informatik.uni-freiburg.de |
| Pseudocode | Yes | Algorithm 1: Overall BDD-based Algorithm Algorithm 2: Adding state pairs Algorithm 3: Primitive actions and empty methods Algorithm 4: Handling methods with two subtasks |
| Open Source Code | Yes | We implemented the presented approach auto Sym and empirically compared it with other optimal (TO)HTN planners.4 auto Sym is based on the PANDA planning framework in its C++ version panda PI (H oller et al. 2021). Since auto Sym operates on a grounded model, we use parser and grounder of panda PI (Behnke et al. 2020).5 4https://github.com/galvusdamor/panda PIengine Symbolic 5https://github.com/panda-planner-dev/panda PIparser and https://github.com/panda-planner-dev/panda PIgrounder |
| Open Datasets | No | The paper mentions collecting domains from former evaluations of HTN planners and listing them (e.g., Barman, Blocks-GTOHP, Childsnack, etc.) with the total number of instances. However, it does not provide explicit access information (links, DOIs, citations with author/year) for these datasets to indicate public availability. |
| Dataset Splits | No | The paper describes using a set of benchmark instances for evaluation but does not specify any training, validation, or test splits. It focuses on the number of instances solved by each planner. |
| Hardware Specification | Yes | Every planner was given 4 GB of RAM and 30 minutes of runtime on a compute cluster with nodes equipped with two Intel Xeon Gold 6242 32-core CPUs, 20 MB cache and 188 GB shared RAM running Ubuntu 18.04 LTS 64 bit. |
| Software Dependencies | Yes | auto Sym is based on the PANDA planning framework in its C++ version panda PI (H oller et al. 2021). To represent and manipulate BDDs we use CUDD 3.0.0 (Somenzi 2015). |
| Experiment Setup | Yes | Every planner was given 4 GB of RAM and 30 minutes of runtime on a compute cluster with nodes equipped with two Intel Xeon Gold 6242 32-core CPUs, 20 MB cache and 188 GB shared RAM running Ubuntu 18.04 LTS 64 bit. auto Sym uses the default panda PI variable ordering for the BDDs. |