Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
HTN Planning as Heuristic Progression Search
Authors: Daniel Höller, Pascal Bercher, Gregor Behnke, Susanne Biundo
JAIR 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our empirical evaluation shows that the resulting system outperforms the state of the art in search-based HTN planning. |
| Researcher Affiliation | Academia | Daniel H oller EMAIL Pascal Bercher EMAIL Gregor Behnke EMAIL Susanne Biundo EMAIL Institute of Artificial Intelligence, Ulm University, Ulm, Germany |
| Pseudocode | Yes | Algorithm 1: The canonical progression-based HTN planning algorithm as grounded, fringe-based formulation as given by H oller et al. (2018a). Algorithm 2: Optimized algorithm presented by H oller et al. (2018a). Algorithm 3: Novel progression algorithm. |
| Open Source Code | Yes | We call our new system PANDApro (indicating the progression search). The source code of the PANDA planners and all domains and problems are available online at www.uni-ulm.de/in/ki/panda. |
| Open Datasets | Yes | The UM-Translog, Satellite, and Woodworking domains are HTN versions of the corresponding domains known from classical planning. They are further described by Bercher et al. (2014). Smartphone A domain describing the task of operating a smartphone. A description is also given by Bercher et al. (2014). The source code of the PANDA planners and all domains and problems are available online at www.uni-ulm.de/in/ki/panda. |
| Dataset Splits | No | The paper lists several domains used for evaluation (UM-Translog, Satellite, Woodworking, Smart Phone, Rover, Transport, Entertainment, PCP) and states they are available online with the source code. However, it does not specify any training/test/validation splits for these problem instances; it treats them as a set of problems to be solved. |
| Hardware Specification | Yes | All experiments ran on Xeon E5-2660 v3 CPUs with a base frequency of 2.60 GHz, a memory limit of 4 GB and time limit of 10 minutes. |
| Software Dependencies | No | The paper mentions that the search engine was reimplemented in C++ and uses components like PANDA for preprocessing. It also refers to classical heuristics such as hadd, h FF, and h LM -Cut by their respective papers. However, it does not provide specific version numbers for the C++ compiler, PANDA, or any other critical software libraries used in their implementation. |
| Experiment Setup | Yes | All experiments ran on Xeon E5-2660 v3 CPUs with a base frequency of 2.60 GHz, a memory limit of 4 GB and time limit of 10 minutes. We included 27 heuristic search configurations of our system: {Alg. 1, Alg. 2, Alg. 3} {rcadd, rc FF, rc LM-Cut} {G, A , WA }. |